research paper about physical development of infants and toddlers

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• Published: 07 January 2021

Physical activity and prospective associations with indicators of health and development in children aged <5 years: a systematic review

• Sanne L. C. Veldman   ORCID: orcid.org/0000-0003-4876-1637 1 ,
• Mai J. M. Chin A Paw 1 &
• Teatske M. Altenburg 1  

International Journal of Behavioral Nutrition and Physical Activity volume  18 , Article number:  6 ( 2021 ) Cite this article

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Early childhood is a critical period for growth and development, yet the association with physical activity during this important period is unknown. The aim of this review is to critically summarize the evidence on the prospective associations between physical activity and health and development in children aged < 5 years.

A systematic search in three electronic databases (Pubmed, PsycINFO, and Sportdiscus) was conducted to identify prospective studies examining the associations between physical activity (all types; specified by quantity) and health indicators (body composition, cardiometabolic health, bone health and risks/harm) or development (motor, cognitive and social-emotional development) in young children (mean age < 5 years at baseline). Two independent researchers assessed the methodological quality using the ‘Quality Assessment Tool for Quantitative Studies’ (EPHPP). This tool covers eight quality criteria: selection bias, study design, confounders, blinding, data collection methods, withdrawals and drop-outs, intervention integrity and data-analysis.

Thirty-nine studies, predominantly conducted in preschoolers (ages 3–5 years), were included of which nine were rated as high methodological quality. There was moderate evidence for a positive association between physical activity and motor ( n  = 11 studies) and cognitive development ( n  = 10 studies) based on consistent findings from studies having low-to-moderate methodological quality. There was insufficient evidence for an association between physical activity and body composition ( n  = 15 studies), cardiometabolic health indicators ( n  = 7 studies), social-emotional development ( n  = 2 studies) and bone health ( n  = 2 studies) based on inconsistent findings from studies having weak-to-high methodological quality.

Conclusions

There is a need for more high-quality research in order to determine the dose-response relationship between physical activity and health and development in early childhood. Special attention should be paid to studies in children below the age of 3 years.

The beneficial impact of physical activity on physical, social and cognitive health indicators is well-known in school-aged children [ 1 , 2 ]. Dose-response relationships indicate the more physical activity the larger the health benefits, and at least moderate intensity physical activity is needed for substantial health benefits [ 2 ]. Interestingly, less is known about the association of physical activity with health indicators or development in children below the age of five. At this young age, children go through a critical period of growth and development as their brain develops rapidly [ 3 ]. It is therefore of great importance to determine the optimal dose of physical activity for this age group to enhance health and development. Previous reviews on the association of physical activity with health and development in early childhood [ 4 , 5 , 6 ] have led to different conclusions, due to a combination of different inclusion criteria (e.g. regarding study design or outcome measure) and approach for the evidence synthesis (e.g. considering the methodological quality of studies). None of the previous reviews have summarized evidence regarding the optimal dose of physical activity for this age group.

The reviews by Timmons et al. (2012) and Carson at al. (2017) both included health indicators (e.g. adiposity and cardio-metabolic health) and developmental (e.g. motor and cognitive development) outcome measures, focused on children with a mean age below 5 years and included English as well as French publications [ 4 , 5 ]. Timmons et al. only included prospective designs and reported results per age group (infants: 0–1 years, toddlers: 1–3 years and preschoolers: 3–5 years) whereas the review by Carson et al. included both prospective and cross-sectional study designs and reported results per design and overall. Both reviews assessed the quality of the included studies using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) framework [ 4 , 5 , 7 ], scoring the criteria: risk of bias (using the Cochran risk of bias assessment [ 8 ]), inconsistency, indirectness, imprecision and other (e.g., dose-response evidence). The quality of evidence was downgraded following limitations associated with these criteria. An important notion here is that in the review of Timmons et al., a subjective measure of physical activity (e.g. parent-report) did not influence the study’s quality if this was the only weak item [ 4 ] whereas in the review of Carson et al., the use of a convenience sample and performance bias did not result in the downgrading of evidence [ 5 ]. Despite differences in reporting of outcomes (e.g. per age group or per study design), both reviews reported positive associations between physical activity and motor development, cognitive development, psychosocial health, bone and skeletal health and cardio metabolic health for one or more age groups or research designs. Timmons et al. also reported a positive association between physical activity and adiposity [ 4 ], whereas Carson et al. reported mixed findings for this association [ 5 ].

Pate et al. (2019) recently reviewed the evidence on the prospective association between physical activity and health outcomes in children up to the age of 6 years [ 6 ]. Risk of bias was assessed using the USDA Nutrition Evidence Library Bias Assessment Tool for original research [ 9 , 10 ], with all items equally contributing to the overall quality scoring. Pate et al. concluded physical activity was beneficial for adiposity and bone health [ 6 ]. These results are similar to Timmons et al. [ 4 ] and partly in line with results by Carson et al. (only for bone health) [ 5 ]. In contrast to the other reviews, evidence for an association between physical activity and cardio metabolic health was insufficient and they did not consider outcomes related to children’s development [ 6 ]. For all reviews, most included studies were conducted in preschoolers (children aged 3–5 years).

Specifying the actual physical activity dose or contrast in exposure versus the reference group is essential for concluding on the association of physical activity with health and development. Therefore, and in contrast with previous reviews, the current review will apply strict inclusion criteria regarding physical activity dose (e.g. specified quantity). Additionally, the reviews described above have applied different approaches to review the literature. As such, and in combination with an increased interest in early childhood over recent years, there is a need to review the current evidence on the association of physical activity with health indicators and development in young children. The aim of this article is to critically summarize the evidence on the prospective association of physical activity with health indicators and development in children aged < 5 years. When possible, the optimal dose of physical activity will be explored by conducting a meta-analysis.

Protocol and registration

This systematic review was registered with the International Prospective Register of Systematic Reviews (PROSPERO), registration number: CRD42019144677. The review followed the guidelines of the Preferred Reporting Items of Systematic Reviews and Meta-analysis (PRISMA) statement [ 11 ].

Eligibility criteria

Studies were included if they met the following criteria: 1) prospective study design (observational cohort or experimental study); 2) physical activity (all types included e.g. prone position in infants and outside play in toddlers) was assessed or clearly described (in case of experimental study) by quantity in apparently healthy children during early childhood (mean age at baseline < 5 years) and examined a prospective association with at least one of the following health indicators and developmental outcomes: motor development (e.g. gross or fine motor skills), cognitive development (e.g. executive functions, language development, concentration), social-emotional development (e.g. self-efficacy, stress, hyperactivity/impulsivity), body composition (e.g. overweight, body mass index [BMI], %body fat), growth (e.g. head circumference), bone health (e.g. bone mineral density), cardio metabolic health (e.g. fitness, blood pressure) and risks/harms (e.g. injury). For experimental studies, a difference in amount of physical activity between intervention and control group needed to be clearly described or measured; 3) article was published in English, in a peer-reviewed journal.

Literature search and study selection

Systematic literature searches were carried out in three electronic databases: PubMed, SportDiscus (Ebsco) and PsychINFO. The search strategy focused on terms referring to study design, population, exposure and outcome measures which were linked by AND combinations. Additional file  1 provides the search strategy. Terms related to physical activity, sedentary behavior and sleep were all included as exposure. For this review, only results regarding physical activity are presented. An updated search, using only the physical activity search terms as exposure was completed on November 21st, 2019.

After removal of duplicates, one reviewer (SV) screened all titles and abstracts and 30% was independently screened by a second reviewer (RH and TA). In case of doubt, studies were included at this stage. For the updated search, all titles and abstract were screened by two reviewers (SV and TA). Full texts were independently screened by two researchers (SV and TA) to determine whether inclusion criteria were met. A third reviewer (MC) was consulted in case of inconsistencies. If a decision could not be made due to missing information (including no access to a full text article), the authors were contacted by email. Reference lists of included studies were scanned for additional relevant studies.

Data extraction

The following data were extracted using a structured form: study methodology (e.g. design, study duration, points of data collection), participants (e.g. sample size, mean age, percentage girls), exposure (e.g. type and amount of physical activity, measurement), outcomes (e.g. outcome measure, measurement) and results. One reviewer (SV) extracted data of all included studies. A second reviewer (TA) independently extracted data of 25% of the included studies and checked the extracted data of the remaining studies. Discrepancies after the 25% data extraction by two independent reviewers was discussed until consensus was reached before the other 75% of the data extraction was performed and checked.

Quality assessment

Two researchers (SV and TA) independently rated the methodological quality of all included studies using an adjusted version of the ‘Quality Assessment Tool for Quantitative Studies’ (EPHPP) [ 12 , 13 ] (see Additional file  2 ). This tool contains 19 items divided over eight quality criteria: selection bias, study design, confounders, blinding, data collection methods, withdrawals and drop-outs, intervention integrity and analysis. The quality criteria blinding and intervention integrity were only applied to intervention studies. Per quality criterion, a quality score was assessed: good, fair or poor. Discrepancies were discussed until consensus was reached. The overall methodological quality of a study was classified as ‘high’ when at most one quality criteria was rated as poor and two as fair. A study was classified as ‘moderate’ when at most two quality criteria were rated as poor. The overall methodological quality of a study was classified as ‘weak’ when more than two quality criteria were rated as poor.

Synthesis of evidence

A best evidence synthesis was applied for each of the health and developmental outcomes to draw conclusions on the level of evidence for a prospective association between physical activity and health indicators and development in children aged < 5 years. This synthesis was based on the number of studies, their methodological quality and the consistency of findings [ 14 , 15 ]:

Strong evidence: consistent findings in multiple studies (≥2) of high methodological quality.

Moderate evidence: consistent findings in one study of high methodological quality and at least one study of weak or moderate methodological quality or consistent findings in multiple studies (≥2) of weak or moderate methodological quality.

Insufficient evidence: only one study available, or inconsistent findings in multiple studies (≥2).

No evidence: consistent findings for the absence of an association in multiple studies (≥2) of moderate or high methodological quality.

Results were considered consistent when ≥75% of studies demonstrated findings in the same direction, which was defined by a significance of p <  0.05 of the fully adjusted model. If studies examined multiple associations for the same health indictor or developmental outcome (e.g. analyzing multiple outcome measures for one health indicator), they were considered to add evidence when consistently demonstrating an association (consistent findings in ≥75% of examined associations). If two or more studies of high methodological quality were available, results of studies with weak methodological quality were ignored in determining the level of evidence.

Meta-analyses

For each of the health and developmental outcomes it was checked whether studies were homogenous in terms of measurement of physical activity, health and developmental outcome, statistical analyses and reported types of effect sizes. As included studies varied to a large extent on these aspects, it was not possible to pool the studies examining the same health or developmental outcomes and conduct a meta-analysis.

Study selection

Figure  1 presents the flow diagram of included studies. The initial search identified 26,401 hits and the updated search identified 2110 hits. After removing duplicates ( n  = 2604) and checking eligibility, 21 relevant studies were eligible for inclusion. An additional 18 studies were included by scanning reference lists of included studies, resulting in a total of 39 eligible studies.

Flow Diagram for the identification, screening, eligibility and inclusion of studies

Study characteristics

The 39 studies included 15,537 participants across 15 countries. Eighteen studies had a longitudinal design [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ] and 21 studies an experimental design [ 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ] of which 11 were randomized [ 34 , 35 , 36 , 38 , 39 , 40 , 41 , 43 , 44 , 46 , 47 ]. The study duration varied between 12 months and 8 years for longitudinal studies and between 18 days and 24 months for experimental studies. Additionally, three studies examined ‘acute’ intervention effects [ 41 , 48 , 49 ]. Sample sizes varied between 16 and 4253 children and the percentage of girls between 23 and 69%. Six studies were conducted in children younger than 12 months [ 24 , 25 , 31 , 38 , 43 , 54 ], two studies in children between one and 3 years [ 19 , 20 ], and 31 studies in children between three and 5 years [ 16 , 17 , 18 , 21 , 22 , 23 , 26 , 27 , 28 , 29 , 30 , 32 , 33 , 34 , 35 , 36 , 37 , 39 , 40 , 41 , 42 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 ]. In 46% of the studies, physical activity was assessed using an objective measurement instrument (e.g. by accelerometer or heart rate monitor). Six different health indicators and development outcomes were examined, with body composition ( n  = 15 studies [ 30 ]) [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 31 , 33 , 34 , 37 , 40 , 42 ], motor development ( n  = 11 studies) [ 25 , 35 , 36 , 37 , 38 , 42 , 43 , 45 , 52 , 53 , 54 ] and cognitive development ( n  = 10 studies) [ 24 , 39 , 41 , 44 , 46 , 47 , 48 , 49 , 50 , 51 ] most frequently reported. Seven studies examined more than one indicator of health and development [ 16 , 24 , 30 , 33 , 36 , 37 , 42 ]. Additional file  3 includes Tables S1-S6 that display details on study design, sample, exposure, outcome, and main findings for all included studies.

Nine out of 39 studies were rated as high methodological quality [ 18 , 20 , 21 , 23 , 27 , 28 , 31 , 32 , 33 ], eight studies were rated as moderate quality [ 16 , 17 , 19 , 22 , 24 , 25 , 26 , 29 ] and 22 studies were rated as weak methodological quality [ 30 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ]. Figure  2 displays a summary of the methodological quality across included studies per outcome. Overall, the item ‘selection bias’ received the lowest score (item A; see Table S7 in Additional file  4 ). For intervention studies, the items ‘intervention integrity’ and ‘blinding’ (items D and G) received the lowest scores.

Summary of methodological quality across reviewed studies per outcome

Data synthesis

A summary of results per health- and developmental outcome is displayed in Table  1 .

• Body composition

Fifteen studies examined the association between physical activity and body composition [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 30 , 31 , 33 , 34 , 37 , 40 , 42 ], of which four studies were intervention studies [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 30 , 31 , 33 ] (See Table 1 and Table S 1 ). Body mass index (BMI) was the most commonly examined outcome measure ( n  = 13 studies) [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 33 , 34 , 37 , 40 , 42 ], followed by fat mass ( n  = 7 studies) [ 16 , 17 , 22 , 23 , 31 , 33 , 34 ], fat percentage ( n  = 6 studies) [ 20 , 22 , 34 , 37 , 40 , 42 ], fat-free mass ( n  = 4 studies) [ 16 , 22 , 23 , 34 ], weight ( n  = 2 studies) [ 19 , 22 ] and waist/hip ratio ( n  = 1 study) [ 30 ]. Nine studies measured physical activity objectively (e.g. by accelerometer) [ 16 , 17 , 19 , 21 , 22 , 23 , 31 , 33 , 34 ] and six studies subjectively (e.g. by parent-report) [ 18 , 20 , 30 , 37 , 40 , 42 ]. Six of the 15 included studies were rated as high quality [ 18 , 20 , 21 , 23 , 31 , 33 ], four studies as moderate quality [ 16 , 17 , 19 , 22 ] and five studies as weak quality [ 30 , 34 , 37 , 40 , 42 ].

Five of six high quality studies examined the association between physical activity and BMI [ 18 , 20 , 21 , 23 , 33 ]. Four studies found no significant association between physical activity and BMI [ 18 , 20 , 23 , 33 ] whereas one study found a positive association [ 21 ]. Three studies examined the association between physical activity and fat mass, of which one study found a negative association [ 31 ] and two studies found no significant association [ 23 , 33 ]. One study examined the association between physical activity and fat percentage and found a negative association for girls but not for boys [ 20 ]. No significant association was found between physical activity and fat free mass [ 23 ].

Based on inconsistent findings among the studies with high methodological quality, there is insufficient evidence for an association between physical activity and body composition in children under the age of 5 years.

• Motor development

Eleven studies examined the association between physical activity and motor development outcomes [ 25 , 35 , 36 , 37 , 38 , 42 , 43 , 45 , 52 , 53 , 54 ] (See Table 1 and Table S 2 ). Outcome measures included total motor scores (gross motor, fine motor or a combination; n  = 7 studies) [ 25 , 37 , 38 , 43 , 45 , 53 , 54 ], a specific component of gross or fine motor skills (e.g. ball skills; n  = 5 studies) [ 35 , 38 , 52 , 53 , 54 ] and individual motor skills (e.g. jump, n  = 8 studies) [ 25 , 35 , 36 , 38 , 42 , 43 , 53 , 54 ]. Ten studies were intervention studies [ 35 , 36 , 37 , 38 , 42 , 43 , 45 , 52 , 53 , 54 ] with a large variety in frequency of implemented physical activity sessions (one session per week to daily), session duration (15 to 60 min per session), study duration (3 weeks to 2 years) and intervention content (e.g. time in prone position in infants, movement program in preschoolers). Physical activity was measured subjectively in all studies. One of the eleven studies was rated as moderate quality [ 25 ] whereas ten studies were rated as weak quality [ 35 , 36 , 37 , 38 , 42 , 43 , 45 , 52 , 53 , 54 ].

All intervention studies found a positive association between physical activity and motor development (either total score, a specific component or an individual skill). Two of these studies only found an association for separate skills or components but not for the total (gross) motor score [ 53 , 54 ] and one study did not perform statistical analyses [ 42 ]. The longitudinal study found a positive association between prone experience and duration and the attainment of motor milestones [ 25 ].

Based on consistent findings among studies with weak-to-moderate methodological quality, there is moderate evidence for a positive association of physical activity with motor development in children under the age of 5 years.

• Cognitive development

Ten studies examined the association between physical activity and cognitive outcomes of which nine studies were intervention studies [ 39 , 41 , 44 , 46 , 47 , 48 , 49 , 50 , 51 ] (See Table 1 and Table S 3 ). Seven studies examined associations with school-related outcomes such as language and math [ 24 , 39 , 44 , 46 , 47 , 50 , 51 ] and three studies examined acute effects on attention, concentration and/or response inhibition [ 41 , 48 , 49 ]. Physical activity was measured both objectively (e.g. accelerometer; n  = 6 studies) [ 39 , 41 , 44 , 46 , 47 , 49 ] and subjectively ( n  = 4 studies) [ 24 , 48 , 50 , 51 ]. One study was rated as moderate quality [ 24 ] whereas nine studies were rated as weak quality [ 39 , 41 , 44 , 46 , 47 , 48 , 49 , 50 , 51 ].

Four intervention studies, lasting between one and 4 weeks, examined the effects of physical activity on learning outcomes [ 39 , 44 , 46 , 47 ], randomly assigning children to: 1) an integrated physical activity condition including task-relevant physical activity, 2) a non-integrated physical activity condition involving task-irrelevant physical activity, 3) a control condition without physical activity. All four studies showed that children in the task-related physical activity group scored best on learning outcomes [ 39 , 44 , 46 , 47 ]. Two studies, lasting six and 8 months respectively, examined the association of physically active academic lessons on early literacy and language in comparison to a regular academic lesson control group and showed positive intervention effects [ 50 , 51 ]. Three intervention studies examined the acute effects of physical activity (range 10–30 min) on attention, concentration and/or response inhibition [ 41 , 48 , 49 ]. The intensity was described as moderate-to-vigorous intensity physical activity in two studies [ 41 , 49 ] while in the third study the intervention was described as ‘recess time’ without specified intensity [ 48 ]. Results on acute effects of physical activity were mixed. One study showed a positive effect of 30-min physical activity on classroom attention [ 47 ], one study did not find an effect of two 10-min physical activity breaks on concentration [ 53 ] and one study showed a positive effect of 20-min physical activity recess compared to 10- or 30-min recess on attention and concentration [ 48 ].

Based on consistent findings among studies with weak-to-moderate methodological quality, there is moderate evidence for a positive association of physical activity with cognitive development in children under the age of 5 years.

Cardiometabolic health indicators

Seven studies examined the association between physical activity and cardiometabolic health indicators such as blood pressure [ 27 , 33 , 36 , 42 ], biomarkers [ 30 , 32 , 33 ] and physical fitness [ 16 , 27 ], of which two studies were intervention studies [ 36 , 42 ] (See Table 1 and Table S 4 ). Physical activity was measured objectively in four studies [ 16 , 27 , 32 , 33 ] and subjectively in three studies [ 30 , 36 , 42 ]. Three studies were rated as high methodological quality [ 27 , 32 , 33 ], one study was rated as moderate methodological quality [ 16 ] and three studies were rated as weak methodological quality [ 30 , 36 , 42 ].

Of the three studies rated as high quality, two studies examined the association between physical activity and blood pressure [ 27 , 33 ]. One study found mixed results (positive association in boys but not in girls) [ 33 ] whereas the other study found no significant association between physical activity and blood pressure [ 27 ]. Two studies found some positive associations when examining the association between physical activity and biomarkers, i.e. adiponectin [ 32 ], metabolic z-scores [ 33 ] and triglycerides [ 33 ]. A positive association between physical activity and physical fitness was found in one study [ 27 ].

Based on inconsistent findings among the studies with high methodological quality, there is insufficient evidence for associations of physical activity with cardiometabolic health indicators in children under the age of 5 years.

• Social-emotional development

Two studies examined the association between physical activity and social-emotional development [ 24 , 26 ] (See Table 1 and Table S 5 ). Both studies had a longitudinal design, measured physical activity subjectively and were rated as moderate methodological quality [ 24 , 26 ]. One study observed an increase in externalizing behavior with increasing physical activity [ 24 ], whereas the other study did not find an association between physical activity and quality of life [ 26 ].

Based on inconsistent findings in two studies of moderate methodological quality, there is insufficient evidence for an association of physical activity with social-emotional development in children under the age of 5 years.

• Bone health

Two studies examined the association between physical activity and bone health: fractures [ 29 ] and bone density [ 28 ] (See Table 1 and Table S 6 ). Both studies had a longitudinal design [ 28 , 29 ] and physical activity was measured objectively in one study [ 28 ]. The methodological quality was rated as high [ 28 ] and moderate quality [ 29 ]. No significant association between physical activity and bone density was found [ 28 ] while time spent in outdoor play in summer was associated with an increased risk of fractures [ 29 ].

Based on inconsistent findings in two studies of moderate-to-high methodological quality, there is insufficient evidence for an association of physical activity with bone health in children under the age of 5 years.

This systematic review summarized the evidence on the prospective associations of physical activity with health indicators and development in children aged < 5 years. We found moderate evidence for a positive association of physical activity with motor development and cognitive development. Although associations were consistently positive, the methodological quality of most of the included studies was weak. For other outcomes, such as body composition, cardiometabolic health indicators, social-emotional development and bone health, the evidence was insufficient due to inconsistent findings.

Conclusions from the current review were partly in line with previous reviews. Results relating to motor and cognitive development were comparable, but in contrast to previous reviews, the current review found no evidence for an association between physical activity and body composition [ 4 , 6 ], bone health [ 4 , 5 , 6 ], cardiometabolic health [ 4 , 5 ] or social-emotional development [ 4 , 5 ]. One explanation might be the difference in inclusion criteria. Compared to the other systematic reviews, we applied stricter inclusion criteria especially regarding the exposure variable physical activity. Studies were only included if the amount of physical activity was quantified and for experimental studies, a difference in amount of physical activity between intervention and control group needed to be clearly described or measured. In many studies it was not clear whether children in the intervention group were exposed to more physical activity than children in the control group, which led to exclusion. In terms of methodological quality assessment, all reviews assessed similar quality items but two reviews excluded some items (e.g. no downgrading of evidence for a subjective measure of physical activity, the use of a convenience sample or performance bias) from the overall quality score [ 4 , 5 ]. In the present review, we included all quality items in the overall methodological quality score and applied an evidence synthesis to combine number of studies, methodological quality and consistency of findings. This, in combination with the difference in included studies, resulted in somewhat different conclusions.

An important methodological limitation is that more than half of the studies did not used valid and reliable measures of physical activity. One reason is the absence of valid and reliable physical activity assessment tools for this young age group, especially in children under the age of 2 years. This might explain the low number of available studies exploring the association of physical activity with health indicators and development in children under the age of 3 years. In addition, a large variation in physical activity was reported (e.g. time spent in moderate-to-vigorous physical activity, physical education or aerobic dance). This prohibited drawing conclusions regarding the optimal type, intensity, frequency and duration of physical activity for health and developmental benefits. Other common methodological issues identified in this review were studies not including a representative sample and the lack of reporting on recruitment rates.

Body composition was the most frequently examined outcome, which is in line with previous reviews [ 4 , 5 , 6 ]. One explanation for insufficient evidence for an association between physical activity and body composition may be the lack of adjustment for diet. Diet is an important component of bodyweight and as such should be adjusted for in analysis when examining the association between physical activity and body weight. As diet was not adjusted for in every study, this could explain the difference in results found and therefore the conclusion insufficient evidence. Furthermore, inaccurate measures of physical activity can explain the results.

We found moderate evidence for a positive association of physical activity with motor development, which is in line with several previously published reviews [ 4 , 5 , 55 ]. An important note is that most of the included studies on motor development were intervention studies. As such, the positive association between physical activity and motor development in the current review may be attributed to the instruction on the quality of movement (e.g. motor skill instruction during interventions) rather than the amount of physical activity itself. When examining the literature on motor skills interventions, the effectiveness of these interventions at improving motor skills through motor skill instruction has been demonstrated at several occasions [ 56 , 57 , 58 ].

Results from the current review confirm the previous evidence on the association of physical activity with cognitive development in children [ 5 , 55 , 59 , 60 ]. However, included studies varied strongly in the amount of physical activity, the study duration, the cognitive outcomes examined and the measurement tools used. Additionally, there is a lack of studies of high methodological quality. As such, more high-quality research is needed to confirm this association, both potential acute as well as long-term effects, that allows for dose-response analysis.

Strengths and limitations

The strength of this review was the inclusion of studies with a prospective design and the strict inclusion criteria regarding physical activity volume. Additional strengths include the thorough and extensive literature search, the contacting of authors in case of missing information, the large number of outcome variables included in the search, the methodological quality assessment and the best evidence synthesis. The main limitation of this review is the heterogeneity of included studies examining the same health or developmental outcome making statistical pooling of included studies inappropriate. Therefore, we could not conduct dose-response meta-analyses. Furthermore, we could not evaluate potential publication bias or selective reporting of significant findings, which is a limitation of our review. Another limitation is only including publications in English.

Recommendations for future research

To increase the evidence-base for physical activity guidelines for the early years, we have the following recommendations for future research:

Conducting high quality randomized controlled trials and prospective cohort studies to examine the dose-response relationships between physical activity and health indicators and developmental outcomes such as body composition, bone and skeletal health, cardiometabolic health, motor development, cognitive development and social-emotional development, especially for children under the age of 3 years. Additionally, to enable future meta-analyses, we urge for consensus on outcome measures (preferably developing a core outcome set [ 61 ]) including preferred valid and reliable assessment tools;

Develop and validate methods for accurate measurement of physical activity in the early years, especially for children under the age of 2 years; and

Improve the quality of reporting studies especially regarding recruitment, blinding of outcome assessors and intervention integrity such as intervention delivery, consistency and potential contamination between intervention and control group.

This systematic review examined the evidence on the prospective association of physical activity with health indicators and development in children aged < 5 years. We found moderate evidence for a positive association of physical activity with motor and cognitive development. We found insufficient evidence for an association of physical activity with body composition, cardio-metabolic health indicators, social-emotional development and bone health. More high-quality research is needed to identify optimal types, intensity, frequency and duration of physical activity for health and developmental that can inform physical activity guideline development for the early years. Special attention should be given to children below the age of 3 years, as in this young age group only few studies are available.

Availability of data and materials

Not applicable.

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Acknowledgements

We would like to thank Ralf Höppener for his assistance with screening titles and abstracts for this review.

The contribution of SV was funded by the Knowledge Center for Sport & Physical Activity, the Netherlands Organization for Health Research and Development (ZonMw; Project No. 546003008) and Bernard van Leer Foundation. The funding bodies had no role in the design of the study; in the collection, analysis, and interpretation of data; or in the writing of the manuscript.

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SV, TA and MC were responsible for the initiation, conceptualization and design of the systematic review. SV coordinated the review and conducted the literature searches. SV and TA conducted the screenings, extracted the data and conducted the quality assessments. SV drafted the manuscript and TA assisted. All authors were responsible for critically revising the manuscript for important intellectual content. All authors approved the final manuscript.

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Additional file 1..

Overview of search terms – This additional file contains the search terms used to conduct the search across three electronic databases: PubMed, SportDiscus (Ebsco) and PsychINFO. The search strategy focused on terms referring to study design, population, exposure and outcome measures which were linked by AND combinations.

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Quality assessment tool for quantitative studies – This additional file contains the adjusted version of the ‘Quality Assessment Tool for Quantitative Studies’ (EPHPP). This tool was used to assess the methodological quality of the included studies.

Additional file 3: Tables S1-6.

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This additional file includes Table S7 that displays the methodological quality of all included studies.

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Veldman, S.L.C., Chin A Paw, M.J.M. & Altenburg, T.M. Physical activity and prospective associations with indicators of health and development in children aged <5 years: a systematic review. Int J Behav Nutr Phys Act 18 , 6 (2021). https://doi.org/10.1186/s12966-020-01072-w

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Chapter 10: Physical Development in Infancy & Toddlerhood

Chapter 10 learning objectives.

• Summarize overall physical growth during infancy.
• Describe the growth in the brain during infancy.
• Explain infant sleep.
• Identify newborn reflexes.
• Compare gross and fine motor skills.
• Contrast the development of the senses in newborns.
• Describe the habituation procedure.
• Explain the merits of breastfeeding and when to introduce more solid foods.
• Discuss the nutritional concerns of marasmus and kwashiorkor.

Overall Physical Growth

The average newborn in the United States weighs about 7.5 pounds (between 5 and 10 pounds) and is about 20 inches in length. For the first few days of life, infants typically lose about 5 percent of their body weight as they eliminate waste and get used to feeding. This often goes unnoticed by most parents but can be cause for concern for those who have a smaller infant. This weight loss is temporary, however, and is followed by a rapid period of growth. By the time an infant is 4 months old, it usually doubles in weight and by one year has tripled the birth weight. By age 2, the weight has quadrupled, so we can expect that a 2-year-old should weigh between 20 and 40 pounds. The average length at one year is about 29.5 inches and at two years it is around 34.4 inches (Bloem, 2007).

Body Proportions

Another dramatic physical change that takes place in the first several years of life is the change in body proportions. The head initially makes up about 50 percent of our entire length when we are developing in the womb. At birth, the head makes up about 25 percent of our length, and by age 25 it comprises about 20 percent our length.

The Brain in the First Two Years

Some of the most dramatic physical change that occurs during this period is in the brain. We are born with most of the brain cells that we will ever have; that is, about 85 billion neurons whose function is to store and transmit information (Huttenlocher&Dabholkar, 1997). While most of the brain’s neurons are present at birth, they are not fully mature. During the next several years dendrites, or branching extensions that collect information from other neurons , will undergo a period of exuberance. Because of this proliferation of dendrites, by age two a single neuron might have thousands of dendrites. Synaptogenesis, or the formation of connections between neurons , continues from the prenatal period forming thousands of new connections during infancy and toddlerhood. This period of rapid neural growth is referred to as synaptic blooming.

The blooming period of neural growth is then followed by a period of synaptic pruning, where neural connections are reduced thereby making those that are used much stronger . It is thought that pruning causes the brain to function more efficiently, allowing for mastery of more complex skills (Kolb & Whishaw, 2011). The experience will shape which of these connections are maintained and which of these are lost. Ultimately, about 40 percent of these connections will be lost (Webb, Monk, and Nelson, 2001). Blooming occurs during the first few years of life, and pruning continues through childhood and into adolescence in various areas of the brain.

Another major change occurring in the central nervous system is the development of myelin, a coating of fatty tissues around the axon of the neuron (Carlson, 2014). Myelin helps insulate the nerve cell and speed the rate of transmission of impulses from one cell to another. This enhances the building of neural pathways and improves coordination and control of movement and thought processes. The development of myelin continues into adolescence but is most dramatic during the first several years of life.

The infant’s brain grows very fast. At birth, the brain is about 250 grams (half a pound) and by one year it is already 750 grams (Eliot, 1999). Comparing to adult size, the newborn brain is approximately 33% of adult size at birth, and in just 90 days, it is already at 55% of adult size (Holland et al., 2014). Most of the neural activity is occurring in the cortex or the thin outer covering of the brain involved in voluntary activity and thinking. The cortex is divided into two hemispheres, and each hemisphere is divided into four lobes, each separated by folds known as fissures. If we look at the cortex starting at the front of the brain and moving over the top (see Figure 3.3), we see first the frontal lobe (behind the forehead), which is responsible primarily for thinking, planning, memory, and judg ment . Following the frontal lobe is the parietal lobe, which extends from the middle to the back of the skull and which is responsible primarily for processing information about touch . Next is the occipital lobe, at the very back of the skull, which processes visual information . Finally, in front of the occipital lobe, between the ears, is the temporal lobe, which is responsible for hearing and language (Jarrett, 2015).

Although the brain grows rapidly during infancy, specific brain regions do not mature at the same rate. Primary motor areas develop earlier than primary sensory areas, and the prefrontal cortex, that is located behind the forehead, is the least developed (Giedd, 2015). As the prefrontal cortex matures, the child is increasingly able to regulate or control emotions, to plan activities, strategize, and have better judgment. This is not fully accomplished in infancy and toddlerhood but continues throughout childhood, adolescence, and adulthood.

Lateralization is the process in which different functions become localized primarily on one side of the brain . For example, in most adults the left hemisphere is more active than the right during language production, while the reverse pattern is observed during tasks involving visuospatial abilities (Springer & Deutsch, 1993). This process develops over time, however, structural asymmetries between the hemispheres have been reported even in fetuses (Chi, Dooling, & Gilles, 1997; Kasprian et al., 2011) and infants (Dubois et al., 2009).

Lastly, neuroplasticity refers to the brain’s ability to change, both physically and chemically, to enhance its adaptability to environmental change and compensate for an injury. The control of some specific bodily functions, such as movement, vision, and hearing, is performed in specified areas of the cortex, and if these areas are damaged, the individual will likely lose the ability to perform the corresponding function. The brain’s neurons have a remarkable capacity to reorganize and extend themselves to carry out these particular functions in response to the needs of the organism, and to repair any damage. As a result, the brain constantly creates new neural communication routes and rewires existing ones. Both environmental experiences, such as stimulation and events within a person’s body, such as hormones and genes, affect the brain’s plasticity. So too does age. Adult brains demonstrate neuroplasticity, but they are influenced less extensively than those of infants (Kolb & Fantie, 1989; Kolb & Whishaw, 2011).

Infant Sleep

A newborn typically sleeps approximately 16.5 hours per 24-hour period. This is usually polyphasic sleep in that the infant is accumulating the 16.5 hours over several sleep periods throughout the day (Salkind, 2005). The infant is averaging 15 hours per 24-hour period by one month, and 14 hours by 6 months. By the time children turn two, they are averaging closer to 10 hours per 24 hours. Additionally, the average newborn will spend close to 50% of the sleep time in the Rapid Eye Movement (REM) phase, which decreases to 25% to 30% in childhood.

Sudden Unexpected Infant Deaths (SUID) : Each year in the United States, there are about 3,500 Sudden Unexpected Infant Deaths (SUID). These deaths occur among infants less than one-year-old and have no immediately obvious cause (CDC, 2019). The three commonly reported types of SUID are:

• Sudden Infant Death Syndrome (SIDS) : SIDS is identified when the death of a healthy infant occurs suddenly and unexpectedly, and medical and forensic investigation findings (including an autopsy) are inconclusive . SIDS is the leading cause of death in Figure 3.4 75 infants 1 to 12 months old, and approximately 1,400 infants died of SIDS in 2017 (CDC, 2019). Because SIDS is diagnosed when no other cause of death can be determined, possible causes of SIDS are regularly researched. One leading hypothesis suggests that infants who die from SIDS have abnormalities in the area of the brainstem responsible for regulating breathing (Weekes-Shackelford & Shackelford, 2005).
• Unknown Cause : The sudden death of an infant less than one year of age that cannot be explained because a thorough investigation was not conducted, and the cause of death could not be determined. In 2017, 1300 infants died from unknown causes (CDC, 2019).
• Accidental Suffocation and Strangulation in Bed : Reasons for accidental suffocation include: Suffocation by soft bedding, another person rolling on top of or against the infant while sleeping, an infant being wedged between two objects such as a mattress and wall, and strangulation such as when an infant’s head and neck become caught between crib railings. In 2017, 900 infants died from accidental suffocation and strangulation.

The combined SUID death rate declined considerably following the release of the American Academy of Pediatrics safe sleep recommendations in 1992, which advocated that infants be placed for sleep on their backs (nonprone position). These recommendations were followed by a major Back to Sleep Campaign in 1994. However, accidental suffocation and strangulation in bed mortality rates remained unchanged until the late 1990s. In 1998 death rates from accidental suffocation and strangulation in bed actually started to increase, and they reached the highest rate at 24.6 deaths per 100,000 live births in 2017 (CDC, 2019).

Should infants be sharing the bed with parents?

Colvin, Collie-Akers, Schunn, and Moon (2014) analyzed a total of 8207 deaths from 24 states during 2004–2012 that were contained in the National Center for the Review and Prevention of Child Deaths Case Reporting System, a database of death reports from state child death review teams. The results indicated that younger victims (0-3 months) were more likely to die by bed-sharing and sleeping in an adult bed/on a person. A higher percentage of older victims (4 months to 364 days) rolled into objects in the sleep environment and changed position from side/back to prone. Carpenter et al. (2013) compared infants who died of SIDS with a matched control and found that infants younger than three months old who slept in bed with a parent were five times more likely to die of SIDS compared to babies who slept separately from the parents but were still in the same room. They concluded that bed-sharing, even when the parents do not smoke or take alcohol or drugs, increases the risk of SIDS. However, when combined with parental smoking and maternal alcohol consumption and/or drug use, risks associated with bed-sharing greatly increased.

The two studies discussed above were based on American statistics. What about the rest of the world? Co-sleeping occurs in many cultures, primarily because of a more collectivist perspective that encourages a close parent-child bond and interdependent relationship (Morelli, Rogoff, Oppenheim, & Goldsmith, 1992). In countries where co-sleeping is common, however, Figure 3.7 Source 77 parents and infants typically sleep on floor mats and other hard surfaces which minimize the suffocation that can occur with bedding (Nelson, Schiefenhoevel, & Haimerl, 2000).

From Reflexes to Voluntary Movements

Table 3.1 Some Common Infant Reflexes

Newborns are equipped with a number of reflexes (see Table 3.1) which are involuntary movements in response to stimulation. Some of the more common reflexes, such as the sucking reflex and rooting reflex, are important to feeding. The grasping and stepping reflexes are eventually replaced by more voluntary behaviors. Within the first few months of life these reflexes disappear, while other reflexes, such as the eye-blink, swallowing, sneezing, gagging, and withdrawal reflex stay with us as they continue to serve important functions. Reflexes offer pediatricians insight into the maturation and health of the nervous system. Reflexes that persist longer than they should and can impede normal development (Berne, 2006). In preterm infants and those with neurological impairments, some of these reflexes may be absent at birth. Once present, they may persist longer than in a neurologically healthy infant (El-Dib, Massaro, Glass & Aly, 2012).

Motor Development

Motor development occurs in an orderly sequence as infants move from reflexive reactions (e.g., sucking and rooting) to more advanced motor functioning. As mentioned during the prenatal section, development occurs according to the Cephalocaudal (from head to tail) and Proximodistal (from the midline outward) principles. For instance, babies first learn to hold their heads up, then to sit with assistance, then to sit unassisted, followed later by crawling, pulling up, cruising or walking while holding on to something, and then unassisted walking (Eisenberg, Murkoff, & Hathaway, 1989). As motor skills develop, there are certain developmental milestones that young children should achieve. For each milestone, there is an average age, as well as a range of ages in which the milestone should be reached. An example of a developmental milestone is a baby holding up its head. Babies on average are able to hold up their head at 6 weeks old, and 90% of babies achieve this between 3 weeks and 4 months old. On average, most babies sit alone at 7 months old. Sitting involves both coordination and muscle strength, and 90% of babies achieve this milestone between 5 and 9 months old. If the child is displaying delays on several milestones, that is reason for concern, and the parent or caregiver should discuss this with the child’s pediatrician. Developmental delays can be identified and addressed through early intervention.

Motor Skills refer to our ability to move our bodies and manipulate objects. Fine motor skills focus on the muscles in our fingers, toes, and eyes, and enable coordination of small actions (e.g., grasping a toy, writing with a pencil, and using a spoon). Newborns cannot grasp objects voluntarily but do wave their arms toward objects of interest. At about 4 months of age, the infant is able to reach for an object, first with both arms and within a few weeks, with only one arm. At this age grasping an object involves the use of the fingers and palm, but no thumbs.  This is known as the Palmer Grasp.  The use of the thumb comes at about 9 months of age when the infant is able to grasp an object using the forefinger and thumb. Now the infant uses a Pincer Grasp, and this ability greatly enhances the ability to control and manipulate an object and infants take great delight in this newfound ability. They may spend hours picking up small objects from the floor and placing them in containers. By 9 months, an infant can also watch a moving object, reach for it as it approaches, and grab it. 

Gross motor skills focus on large muscle groups that control our head, torso, arms and legs and involve larger movements (e.g., balancing, running, and jumping). These skills begin to develop first. Examples include moving to bring the chin up when lying on the stomach, moving the chest up, and rocking back and forth on hands and knees. But it also includes exploring an object with one’s feet as many babies do as early as 8 weeks of age if seated in a carrier or other device that frees the hips. This may be easier than reaching for an object with the hands, which requires much more practice (Berk, 2007). Sometimes an infant will try to move toward an object while crawling and surprisingly move backward because of the greater amount of strength in the arms than in the legs.

Sensory Capacities

Throughout much of history, the newborn was considered a passive, disorganized being who possessed minimal abilities. William James, an early psychologist, had described the newborn’s world as “a blooming, buzzing confusion,” (Shaffer, 1985). However, current research techniques have demonstrated just how developed the newborn is with especially organized sensory and perceptual abilities.

Vision: The womb is a dark environment void of visual stimulation. Consequently, vision is one of the most poorly developed senses at birth, and time is needed to build those neural pathways between the eyes and the brain (American Optometric Association [AOA], 2019). Newborns typically cannot see further than 8 to 10 inches away from their faces (AOA, 2019). An 8-week old’s vision is 20/300. This means an object 20 feet away from an infant has the same clarity as an object 300 feet away from an adult with normal vision. By 3-months visual acuity has sharpened to 20/200, which would allow them the see the letter E at the top of a standard eye chart (Hamer, 2016). As a result, the world looks blurry to young infants (Johnson & deHaan, 2015).

Why is visual acuity so poor in the infant? The fovea, which is the central field of vision in the retina and allows us to see sharp detail, is not fully developed at birth, and does not start to reach adult levels of development until 15 months (Li & Ding, 2017). Even by 45 months some of the sensory neurons (cones) of the fovea are still not fully grown. Can babies see color?

Young infants can perceive color, but the colors need to be very pure forms of basic colors, such as vivid red or green rather than weaker pastel shades. Most studies report that babies can see the full spectrum of colors by five months of age (AOA, 2019).

Newborn infants prefer and orient to face-like stimuli more than they do other patterned stimuli (Farroni et al., 2005). They also prefer images of faces that are upright and not scrambled (Chien, 2011). Infants also quickly learn to distinguish the face of their mother from faces of other women (Bartrip, Morton, & De Schonen, 2001). When viewing a person’s face, one-month olds fixate on the outer edges of the face rather than the eyes, nose, or mouth, and two-month olds gaze more at the inner features, especially the eyes (Hainline, 1978).

Researchers have examined the development of attention and tracking in the visual system and have found the following for young infants:

• One-month-olds have difficulty disengaging their attention and can spend several minutes fixedly gazing at a stimulus (Johnson & deHaan, 2015).
• Aslin (1981) found that when tracking an object visually, the eye movements of newborns and one-month olds are not smooth but saccadic, that is step-like jerky movements. Aslin also found their eye movements lag behind the object’s motion. This means young infants do not anticipate the trajectory of the object. By two months of age, their eye movements are becoming smoother, but they still lag behind the motion of the object and will not achieve this until about three to four months of age (Johnson & deHaan, 2015).
• Newborns also orient more to the visual field toward the side of the head, than to the visual field on either side of the nose (Lewis, Maurer, & Milewski, 1979). By two to three months, stimuli in both fields are now equally attended to (Johnson & deHaan, 2015).

Binocular vision, which requires input from both eyes, is evident around the third month and continues to develop during the first six months (Atkinson & Braddick, 2003). By six months infants can perceive depth perception in pictures as well (Sen, Yonas, & Knill, 2001). Infants who have experience crawling and exploring will pay greater attention to visual cues of depth and modify their actions accordingly (Berk, 2007).

Hearing: The infant’s sense of hearing is very keen at birth, and the ability to hear is evidenced as soon as the seventh month of prenatal development. Newborns prefer their mother’s voices over another female when speaking the same material (DeCasper & Fifer, 1980). Additionally, they will register in utero specific information heard from their mother’s voice.

DeCasper and Spence (1986) tested 16 infants (average age of 55.8 hours) whose mothers had previously read to them prenatally. The mothers read several passages to their fetuses, including the first 28 paragraphs of the Cat in the Hat, beginning when they were 7 months pregnant. The fetuses had been exposed to the stories an average of 67 times or 1.5 hours. When the experimental infants were tested, the target stories (previously heard) were more reinforcing than the novel story as measured by their rate of sucking. However, for control infants, the target stories were not more reinforcing than the novel story indicating that the experimental infants had heard them before. 

An infant can distinguish between very similar sounds as early as one month after birth and can distinguish between a familiar and non-familiar voice even earlier. Infants are especially sensitive to the frequencies of sounds in human speech and prefer the exaggeration of infant-directed speech, which will be discussed later. Additionally, infants are innately ready to respond to the sounds of any language, but between six and nine months they show a preference for listening to their native language (Jusczyk, Cutler, & Redanz, 1993). Their ability to distinguish the sounds that are not in the language around them diminishes rapidly (Cheour-Luhtanen, et al., 1995). 

Touch and Pain: Immediately after birth, a newborn is sensitive to touch and temperature, and is also highly sensitive to pain, responding with crying and cardiovascular responses (Balaban & Reisenauer, 2013). Newborns who are circumcised, which is the surgical removal of the foreskin of the penis, without anesthesia experience pain as demonstrated by increased blood pressure, increased heart rate, decreased oxygen in the blood, and a surge of stress hormones (United States National Library of Medicine, 2016). Research has demonstrated that infants who were circumcised without anesthesia experienced more pain and fear during routine childhood vaccines. Fortunately, today many local pain killers are currently used during circumcision.

Taste and Smell: Studies of taste and smell demonstrate that babies respond with different facial expressions, suggesting that certain preferences are innate. Newborns can distinguish between sour, bitter, sweet, and salty flavors and show a preference for sweet flavors. Newborns also prefer the smell of their mothers. An infant only 6 days old is significantly more likely to turn toward its own mother’s breast pad than to the breast pad of another baby’s mother (Porter, Makin, Davis, & Christensen, 1992), and within hours of birth an infant also shows a preference for the face of its own mother (Bushnell, 2001; Bushnell, Sai, & Mullin, 1989).

Intermodality: Infants seem to be born with the ability to perceive the world in an intermodal way; that is, through stimulation from more than one sensory modality. For example, infants who sucked on a pacifier with either a smooth or textured surface preferred to look at a corresponding (smooth or textured) visual model of the pacifier. By 4 months, infants can match lip movements with speech sounds and can match other audiovisual events. Sensory processes are certainly affected by the infant’s developing motor abilities (Hyvärinen, Walthes, Jacob, Nottingham Chapin, & Leonhardt, 2014). Reaching, crawling, and other actions allow the infant to see, touch, and organize his or her experiences in new ways.

How are Infants Tested:  Habituation procedures, that is measuring decreased responsiveness to a stimulus after repeated presentations, have increasingly been used to evaluate infants to study the development of perceptual and memory skills. Phelps (2005) describes a habituation procedure used when measuring the rate of the sucking reflex.

Researchers first measure the initial baseline rate of sucking to a pacifier equipped with transducers that measure muscle contractions. Next, an auditory stimulus is presented, such as a human voice uttering a speech sound such as “da.” The rate of sucking will typically increase with the new sound, but then decrease to baseline levels as “da” is repeatedly presented, showing habituation. If the sound “ma” was then presented, the rate of sucking would again increase, demonstrating that the infant can discriminate between these two stimuli.

Additionally, the speed or efficiency with which infants show habituation has been shown to predict outcomes in behaviors, such as language acquisition and verbal and nonverbal intelligence. Infants who show difficulty during habituation, or habituate at slower than normal rates, have been found to be at an increased risk for significant developmental delays. Infants with Down syndrome, teratogen-exposed infants, malnourished infants, and premature infants have all been studied. Researchers have found that at the age of 16 months, high-risk infants show rates of habituation comparable to newborn infants (Phelps, 2005).

Breast milk is considered the ideal diet for newborns. Colostrum, the first breast milk produced during pregnancy, and just after birth has been described as “liquid gold” (United States Department of Health and Human Services (USDHHS), 2011). It is very rich in nutrients and antibodies. Breast milk changes by the third to fifth day after birth, becoming much thinner, but containing just the right amount of fat, sugar, water, and proteins to support overall physical and neurological development. For most babies, breast milk is also easier to digest than formula. Formula-fed infants experience more diarrhea and upset stomachs. The absence of antibodies in formula often results in a higher rate of ear infections and respiratory infections. Children who are breastfed have lower rates of childhood leukemia, asthma, obesity, type 1 and 2 diabetes, and a lower risk of SIDS. The USDHHS recommends that mothers breastfeed their infants until at least 6 months of age and that breast milk be used in the diet throughout the first year or two. 

Several recent studies have reported that it is not just babies that benefit from breastfeeding. Breastfeeding stimulates contractions in the uterus to help it regain its normal size, and women who breastfeed are more likely to space their pregnancies further apart. Mothers who breastfeed are at lower risk of developing breast cancer (Islami et al., 2015), especially among higher-risk racial and ethnic groups (Islami et al., 2015; Redondo et al., 2012). Women who breastfeed have lower rates of ovarian cancer (Titus-Ernstoff, Rees, Terry, & Cramer, 2010), reduced risk for developing Type 2 diabetes (Schwarz et al., 2010; Gunderson, et al., 2015), and rheumatoid arthritis (Karlson, Mandl, Hankinson, & Grodstein, 2004). In most studies these benefits have been seen in women who breastfeed longer than 6 months.

Current rates of breastfeeding indicate that 83.2% of mothers have breastfed their infants at some point (CDC, 2018). However, most mothers who breastfeed in the United States stop breastfeeding exclusively at about 6-8 weeks, often in order to return to work outside the home (USDHHS, 2011). Mothers can certainly continue to provide breast milk to their babies by expressing and freezing the milk to be bottle fed at a later time or by being available to their infants at feeding time. However, some mothers find that after the initial encouragement they receive in the hospital to breastfeed, the outside world is less supportive of such efforts. Some workplaces support breastfeeding mothers by providing flexible schedules and welcoming infants, but many do not. In addition, not all women may be able to breastfeed. Women with HIV are routinely discouraged from breastfeeding as the infection may pass to the infant. Similarly, women who are taking certain medications or undergoing radiation treatment may be told not to breastfeed (USDHHS, 2011).  

Besides the nutritional benefits of breastfeeding, breast milk is free. Anyone who has priced formula recently can appreciate this added incentive to  breastfeeding. Prices for a year’s worth of formula and feeding supplies can cost between $1,500 and $3000 per year (Los Angles County Department of Public Health, 2019). In addition to the formula, costs include bottles, nipples, sterilizers, and other supplies.  

One early argument given to promote the practice of breastfeeding was that it promoted bonding and healthy emotional development for infants. However, this does not seem to be the case. Breastfed and bottle-fed infants adjust equally well emotionally (Ferguson & Woodward, 1999). This is good news for mothers who may be unable to breastfeed for a variety of reasons and for fathers who might feel left out.

When to Introduce More Solid Foods: Solid foods should not be introduced until the infant is ready. According to The Clemson University Cooperative Extension (2014), some things to look for include that the infant:

• can sit up without needing support
• can hold its head up without wobbling
• shows interest in foods others are eating
• is still hungry after being breastfed or formula-fed
• is able to move foods from the front to the back of the mouth
• is able to turn away when they have had enough

For many infants who are 4 to 6 months of age, breast milk or formula can be supplemented with more solid foods. The first semi-solid foods that are introduced are iron-fortified infant cereals mixed with breast milk or formula. Typically rice, oatmeal, and barley cereals are offered as a number of infants are sensitive to more wheat-based cereals. Finger foods such as toast squares, cooked vegetable strips, or peeled soft fruit can be introduced by 10-12 months. New foods should be introduced one at a time, and the new food should be fed for a few days in a row to allow the baby time to adjust to the new food. This also allows parents time to assess if the child has a food allergy. Foods that have multiple ingredients should be avoided until parents have assessed how the child responds to each ingredient separately. Foods that are sticky (such as peanut butter or taffy), cut into large chunks (such as cheese and harder meats), and firm and round (such as hard candies, grapes, or cherry tomatoes) should be avoided as they are a choking hazard. Honey and corn syrup should be avoided as these often contain botulism spores. In children under 12 months, this can lead to death (Clemson University Cooperative Extension, 2014). 

Figure 3.12

Global Considerations and Malnutrition

Children in developing countries and countries experiencing the harsh conditions of war are at risk for two major types of malnutrition, also referred to as wasting. Infantile marasmus refers to starvation due to a lack of calories and protein. Children who do not receive adequate nutrition lose fat and muscle until their bodies can no longer function. Babies who are breastfed are much less at risk of malnutrition than those who are bottle-fed.

After weaning, children who have diets deficient in protein may experience kwashiorkor known as the “disease of the displaced child” often occurring after another child has been born and taken over breastfeeding. This results in a loss of appetite and swelling of the abdomen as the body begins to break down the vital organs as a source of protein.

Around the world, the rates of wasting have been dropping. However, according to the World Health Organization and UNICEF, in 2014 there were 50 million children under the age of five that experienced these forms of wasting, and 16 million were severely wasted (UNICEF, 2015). This works out to 1 child in every 13 children in the world suffers from some form of wasting, and the majority of these children live in Asia (34.3 million) and Africa (13.9 million). Wasting can occur as a result of severe food shortages, regional diets that lack certain proteins and vitamins, or infectious diseases that inhibit appetite (Latham, 1997). 

The consequences of wasting depend on how late in the progression of the disease parents and guardians seek medical treatment for their children. Unfortunately, in some cultures families do not seek treatment early, and as a result by the time a child is hospitalized the child often dies within the first three days after admission (Latham, 1997). Several studies have reported long- term cognitive effects of early malnutrition (Galler & Ramsey, 1989; Galler, Ramsey, Salt & Archer, 1987; Richardson, 1980), even when home environments were controlled (Galler, Ramsey, Morley, Archer & Salt, 1990). Lower IQ scores (Galler et al., 1987), poor attention (Galler & Ramsey, 1989), and behavioral issues in the classroom (Galler et al., 1990) have been reported in children with a history of serious malnutrition in the first few years of life.

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Attribution

Adapted from Chapter 3 from Lifespan Development: A Psychological Perspective Second Edition by Martha Lally and Suzanne Valentine-French under the Creative Commons Attribution-Noncommercial-Share Alike 3.0 unported license.

Human Behavior and the Social Environment I Copyright © 2020 by Susan Tyler is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Physical Developmental Milestones: Infants and Toddlers

Young children rapidly grow, develop, and achieve important milestones between birth and age 3, creating the foundation for later growth. Physical development is one domain of infant and toddler development. It relates to changes, growth, and skill development of the body, including development of muscles and senses. This lesson will introduce developmental milestones in addition to influences on early physical growth and development.

• Identify infant and toddler physical and motor developmental milestones and ways to support development for all infants and toddlers.
• Describe the brain’s role in infant and toddler physical development.
• Recognize influences of physical growth and development.

Physical Development From the Start

Healthy babies are born with fully developed systems that allow their bodies to function, such as their ability to suck, swallow, and breathe. In order to support the best possible physical development, all infants require responsive care from loving adults, proper nutrition, and appropriate and stimulating environments. Infant and toddler physical development occurs rapidly over the first years of a child’s life. It is essential that caregivers understand the various stages of infant and toddler physical development so that they can respond to children’s needs appropriately.

Infants are born to explore the world around them. While each child will grow to master many of the stages in physical development on their own schedule, infants are often eager to progress from those innate abilities to further movement of their mouth, eyes, and bodies as they seek people and objects of comfort or interest. They continue practicing skills and building one form of physical movement upon another, step by step as they move closer to desired objects. Through ongoing observation and frequent conversations with families you can learn what infants are able to do, what they’re learning to do, and what areas they are needing your support in.

Infants develop physically from the top down, starting with their head and neck. At birth, an infant has a very difficult time holding up their head because their neck muscles are not strong enough to provide support. As infants and toddlers grow, their determination to master movement, balance, and fine- and gross-motor skills remains strong. Rolling and crawling occur as infants develop skill in using their large-muscle groups. Grasping and picking up objects with fingers are part of small-muscle growth.

Influences on Early Physical Growth and Development

There is no exact age at which all infants should be able to grasp objects or hold up their head without support. Physical development occurs at different times for all children depending on many factors, such as a child’s unique characteristics, the family’s values and culture, and access to available resources. However, many infants and toddlers experience developmental milestones within similar timeframes of growth. The information on the chart below is a comprehensive list of physical development milestones that children typically develop during infancy and toddlerhood . A brief version of this information aimed at parents can be found in an easy-to-use checklist from the Centers for Disease Control and Prevention in the Apply section.

Examples of Physical Development Milestones – Infants and Toddlers

• Holds head up when on tummy
• Kicks both arms and legs while on back
• Briefly relaxes hands from fists for short periods
• Grasps adult finger
• Raises head and chest while lying on stomach
• Primitive reflexes present, including the rooting and sucking reflex
• Holds head steady without support
• Maintains hold of a toy placed in their hands
• Swings arm at objects
• Brings hands to mouth
• Pushes up from ground onto elbows when lying on tummy
• Rolls from tummy to back and may roll from back to tummy
• Pushes arms straight when on tummy
• Uses hands to support when sitting
• Supports weight equally on legs when standing with support
• Moves into the sitting position without support
• Transfers items from one hand to the other
• Uses fingers as a “rake” to pull food and objects towards self
• Sits without support
• Lowers body with control while using support
• Pulls up to stand
• Walks or “cruises” along furniture for support
• Drinks from a cup without a lid with adult support
• Uses thumb and finger “pincer grasp” to pick up small items
• Takes a few steps independently
• Feeds themself finger foods
• Squats to pick up an object from the floor and then stands up without support
• Makes marks on paper using crayon
• Walks independently
• Drinks from a cup
• Tries to use a spoon
• Climbs on and off furniture independently
• Tries to kick a ball after observing an adult

24 Months/ 2 Years

• Kicks a ball
• Walks up and down the stairs while holding on for support
• Eats with a spoon
• Turns doorknobs

30 Months Years

• Uses hands to twist and unscrew objects
• Takes off loose clothing alone
• Jumps off the ground with both feet
• Can turn book pages one at a time

36 Months/ 3 Years

• Strings items onto a string such as large beads or macaroni
• Dresses self in loose clothing
• Uses a fork
• Pedals a tricycle

Source: Centers for Disease Control and Prevention (2021). Developmental Milestones. https://www.cdc.gov/ncbddd/actearly/pdf/FULL-LIST-CDC_LTSAE-Checklists2021_Eng_FNL2_508.pdf  

All children develop at different rates, so keep in mind that the milestones above are simply the average ages at which specific skills are observed.

Certain conditions must exist for an infant or toddler to grow and develop. A young child’s basic physical needs, include:

• Food (nutritious and age-appropriate)
• Shelter (protection from harm)
• Clean air and environment
• Health and dental care
• Activity and rest

We also know that the way we ourselves were raised is important to our understanding of how and in what contexts children develop. The values and beliefs held by our family and culture contribute to our knowledge of growth and development.

Culture Affects How We See and Interpret Behaviors and Development

Understanding the practices, beliefs, and values of the families you support can help you understand how culture shapes so many parts of an infant’s and toddler’s development. Without this understanding, it is difficult to interpret the infant’s or toddler’s behaviors and development. For example, you may believe it is important to help toddlers learn to become independent and begin to feed themselves using their fine motor skills. A family, however, may not view independence as important because they believe it is more valuable to depend upon one another.

Other influences on infant and toddler physical growth and development are:

• Prenatal care and development including, genetic inheritance and makeup, family growth patterns, exposure to drugs and alcohol, and birth experience
• Prematurity (birth before the 38th week of development) and a low birth weight may result in respiration difficulties, vision problems, feeding and digestive problems
• Temperament, or other ways an infant or toddler approaches and interacts with their world
• Family’s composition, lifestyle, level of education, and housing
• Maturation, or the genetic or biological development that reflects a pattern of growth from conception through adolescence
• Developmental delays or disabilities, including health and medical concerns

Review the handout, Infant and Toddler Physical Development located below in the Learn activities section to learn more about important milestones in physical development, as well as variations in the rate of physical development for infants and toddlers.

The Brain’s Role in Physical Development

You can easily observe infants making movements with their bodies and refining their motor skills. Thanks to advances in research and technology, we can now also see how the brain changes and grows as young children develop. At birth, the brain is 25 percent of its adult size, and by age 5, it reaches 90 percent of adult size. Infants’ and toddlers’ early-life interactions and experiences help them make sense of the world and form connections between different parts of the brain.

These supportive experiences and connections help improve coordination and strengthen muscles. As infants repeat and practice different movements, such as turning their heads, rooting, or reaching for an object, they build and maintain connections between brain cells. In essence, the brain is busy making sense of surroundings and learning from experiences.

It is important for infants and toddlers to have time for these new experiences and to explore the world around them with a trusted and caring family child care provider. Repeated exposure to experiences with trusted caregivers allows children to feel safe and secure, and allows them to focus on experimenting, developing, and mastering new skills. The safe place that you create for their exploration ensures that their brains are able to focus on learning, developing, and making connections. If infants and toddlers do not have nurturing and responsive adults to help make them feel safe, their brains will instinctually focus on survival. This kind of stress on a child’s brain may make them more hesitant to engage in exploration, and experimentation; causing them to have less opportunities to create and strengthen connections in the brain that further their growth and skill development.

Supporting Physical Development for All Learners

Physical development, including gross and fine motor skills, consumes the interest of infants and toddlers as they practice learned skills and look to develop new ones. Healthy physical development is dependent on adequate nutrition, brain development, the central nervous system, muscles, bones, and the interactions and experiences offered to infants and toddlers. All children develop at their own pace but recognizing signs of possible developmental delays during infancy or toddlerhood allows early intervention to be more effective than if the delays are not acknowledged until later in childhood. Below are some characteristics of possible physical concerns and developmental delays by various sources:

Signs of Impaired Physical Development - Infants & Toddlers

Delays in physical development may affect more than gross and fine motor skills. For example, if an infant is unable to smile at parents or lift their arms to be picked up, this could affect social and emotional development in terms of relationship building. Recognizing some of the delays listed above can be critical to a child’s development. The connections in a baby’s brain are most adaptable in the first three years of life. These connections, also called neural circuits, are the foundation for learning, behavior, and health. Over time, these connections become harder to change. Early intervention can help children improve their abilities and learn new skills.

If you have concerns about an infant’s or toddler’s physical development, be sure to speak with your coach, trainer, administrator and/or the child’s parent. They may wish to share your concerns with the child’s health-care provider. Early intervention can help children improve their abilities and learn new skills. To find your state or territory’s early intervention contact information, go to: https://www.cdc.gov/ncbddd/actearly/parents/states.html

For more information, including what to say when you contact early intervention and how to get support for your family, visit:  https://www.cdc.gov/ncbddd/actearly/concerned.html

Physical Development in Infants and Toddlers

How can you make sure you are providing age-appropriate experiences to support infant and toddler physical development? Take a moment to read and review the sets of guidelines on the following webpage from SHAPE America (Society of Health and Physical Educators, formerly known as the National Association for Sport and Physical Education, or NASPE): https://www.shapeamerica.org/standards/guidelines/activestart.aspx .

Next, try one or more of the following activities with the infants or toddlers in your care:

• When an infant is awake and active, offer tummy time — lay the baby on the floor on his or her tummy while you interact with the infant. Provide stimulating and high contrast toys or pictures for the infant to look at. Because suffocation is swift and silent, remember to never leave an infant alone when they are on their stomach—not even for a second.
• Hold an infant or dance with a toddler to music. Toddlers can also swing colorful scarves in the air, dance, or play maracas while the music is playing.
• Encourage imitation of gestures and other movement experiences in which mobile infants and toddlers can use their bodies to interact and play.
• Have toddlers experience kicking, catching, rolling, and bouncing balls.
• Encourage toddlers to scribble on paper with crayons.

Incorporate daily physical play into your daily routines. Infants and toddlers enjoy being active!

Infant and Toddler Physical Development

Review the handout, Scenarios: Gross and Fine Motor Development below, and consider what you have learned so far throughout this lesson. In the activity, think about which characteristics or behaviors would be considered fine motor skills and which would be considered gross motor skills. Then write these down and think about possible ways you could support the young children in each scenario.

You can also review the Infant and Toddler Physical Development handout in the Learn section for additional ideas.

Once finished, share your thoughts and responses with your trainer, coach or family child care administrator.

Scenarios: Gross and Fine Motor Development

Consider using the following resources in your family child care program. Use the Milestone Moments document to monitor the physical development of the children in your program. Parents may be interested in the Milestone Tracker Mobile App from the CDC, which they can access using this link: https://www.cdc.gov/ncbddd/actearly/milestones-app.html . The resource, What Grown-Ups Understand About Child Development , is a national benchmark survey sponsored in part by ZERO TO THREE. Read over the survey findings of this study and think about your work with families and the way you gather and share information with them regarding the physical development of the infants and toddlers in your care.

Milestone Moments

Milestone moments - spanish, what grown-ups understand about child development, demonstrate.

Allen, K. E., & Marotz, L. (2001). By the ages: Behavior and development of children pre-birth through eight. Clifton Park, NY: Thomson Delmar Learning.

Berger, S. E., & Adolph, K. E. (2003). Infants use handrails as tools in a locomotor task. Developmental Psychology , 39 : 594-605.

Blakemore, C. (2003). Movement is essential to learning. Journal of Physical Education, Recreation and Dance, 74 (9): 22-25, 41.

Bosco, F. M., Friedman, O., & Leslie, A. M. (2006). Recognition of pretend and real actions in play by 1- and 2-year-olds: Early success and why they fail. Cognitive Development, 21: 1-10.

Bourgeois, K. S., Akhawar, A. W., Neal, S. A., & Lockman, J. J. (2005). Infant manual exploration of objects, surfaces, and their interrelations. Infancy, 8: 233–252.

Centers for Disease Control and Prevention. (2021). Developmental milestones . https://www.cdc.gov/ncbddd/actearly/pdf/FULL-LIST-CDC_LTSAE-Checklists2021_Eng_FNL2_508.pdf

Claxton, L. J., Keen, R., & McCarty, M. E. (2003). Evidence of motor planning in infant reaching behavior. Psychological Science, 14: 354-356.

Clearfield, M. W., Osborne, C. N., & Mullen, M. (2008). Learning by looking: Infants’ social looking behavior across the transition from crawling to walking. Journal of Experimental Child Psychology, 100: 297-307.

Comfort, R. L. (2005). Learning to play: Play deprivation among young children in foster care. Zero to Three, 25: 50-53.

Paul H. Brookes Publishing Co., Inc. (2002). Ages and stages questionnaire(ASQ). https://agesandstages.com/

The National Early Childhood Technical Assistance Center (NECTAC). (2011). The importance of early intervention for infants and toddlers with disabilities and their families . https://files.eric.ed.gov/fulltext/ED522123.pdf

Ward, M., Lee, S., & Lipper, E. (2000). Failure to thrive is associated with disorganized infant-mother attachment and unresolved maternal attachment.  Infant Mental Health Journal, 21 (6): 428-442.

Waters, E., Weinfield, N., & Hamilton, C. (2000). The stability of attachment from infancy to adolescence end early adulthood: General discussion.  Child Development, 71 (3): 703-706.

Zeanah, C. (Ed.). (2000). Handbook of Infant Mental Health (2nd ed.). New York: The Guilford Press.

Infants and toddlers

Infants come into the world with cognitive, emotional, and social capacities that enable them to seek stimulation actively and regulate their own behavior through environmental interactions. They are able to integrate information across the senses, recognize their parents and other caregivers, imitate facial expressions, and manifest distress, contentment, and interest.

At around 18 months, language development and symbolic play enable toddlers to have complex negotiations with caregivers, develop true interactive play with peers, and develop moral emotions such as embarrassment and empathy and, a few months later, guilt, pride, and shame.

Adapted from the Encyclopedia of Psychology

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Cognitive Development in Infants and Toddlers

What you’ll learn to do: explain cognitive development in infants and toddlers.

In addition to rapid physical growth, young children also exhibit significant development of their cognitive abilities, particularly in language acquisition and in the ability to think and reason. You already learned a little bit about Piaget’s theory of cognitive development, and in this section, we’ll apply that model to cognitive tasks during infancy and toddlerhood. Piaget described intelligence in infancy as sensorimotor or based on direct, physical contact where infants use senses and motor skills to taste, feel, pound, push, hear, and move in order to experience the world. These basic motor and sensory abilities provide the foundation for the cognitive skills that will emerge during the subsequent stages of cognitive development.

Learning Outcomes

• Describe each of Piaget’s theories and stages of sensorimotor intelligence
• Explain learning and memory abilities in infants and toddlers
• Describe stages of language development during infancy
• Compare theories of language development in toddlers
• Explain the procedure, results, and implications of Hamlin and Wynn’s research on moral reasoning in infants

Cognitive Development

Cognitive development in children.

In order to adapt to the evolving environment around us, humans rely on cognition, both adapting to the environment and also transforming it. In general, all theorists studying cognitive development address three main issues:

• The typical course of cognitive development
• The unique differences between individuals
• The mechanisms of cognitive development (the way genetics and environment combine to generate patterns of change)

Piaget and Sensorimotor Intelligence

How do infants connect and make sense of what they are learning? Remember that Piaget believed that we are continuously trying to maintain cognitive equilibrium, or balance, between what we see and what we know (Piaget, 1954). Children have much more of a challenge in maintaining this balance because they are constantly being confronted with new situations, new words, new objects, etc. All this new information needs to be organized, and a framework for organizing information is referred to as a schema . Children develop schemas through the processes of assimilation and accommodation .

For example, 2-year-old Deja learned the schema for dogs because her family has a Poodle. When Deja sees other dogs in her picture books, she says, “Look mommy, dog!” Thus, she has assimilated them into her schema for dogs. One day, Deja sees a sheep for the first time and says, “Look mommy, dog!” Having a basic schema that a dog is an animal with four legs and fur, Deja thinks all furry, four-legged creatures are dogs. When Deja’s mom tells her that the animal she sees is a sheep, not a dog, Deja must accommodate her schema for dogs to include more information based on her new experiences. Deja’s schema for dog was too broad since not all furry, four-legged creatures are dogs. She now modifies her schema for dogs and forms a new one for sheep.

Let’s examine the transition that infants make from responding to the external world reflexively as newborns, to solving problems using mental strategies as two-year-olds. Piaget called this first stage of cognitive development  sensorimotor intelligence  (the sensorimotor period) because infants learn through their senses and motor skills. He subdivided this period into six substages:

Substages of Sensorimotor Intelligence

For an overview of the substages of sensorimotor thought, it helps to group the six substages into pairs. The first two substages involve the infant’s responses to its own body, call primary circular reactions . During the first month first (substage one), the infant’s senses, as well motor reflexes are the foundation of thought.

Substage One:  Reflexive Action (Birth through 1st month)

This active learning begins with automatic movements or reflexes (sucking, grasping, staring, listening). A ball comes into contact with an infant’s cheek and is automatically sucked on and licked. But this is also what happens with a sour lemon, much to the infant’s surprise! The baby’s first challenge is to learn to adapt the sucking reflex to bottles or breasts, pacifiers or fingers, each acquiring specific types of tongue movements to latch, suck, breath, and repeat. This adaptation demonstrates that infants have begun to make sense of sensations. Eventually, the use of these reflexes becomes more deliberate and purposeful as they move onto substage two.

Substage Two:  First Adaptations to the Environment (1st through 4th months)

Fortunately, within a few days or weeks, the infant begins to discriminate between objects and adjust responses accordingly as reflexes are replaced with voluntary movements. An infant may accidentally engage in a behavior and find it interesting, such as making a vocalization. This interest motivates trying to do it again and helps the infant learn a new behavior that originally occurred by chance. The behavior is identified as circular and primary because it centers on the infant’s own body. At first, most actions have to do with the body, but in months to come, will be directed more toward objects. For example, the infant may have different sucking motions for hunger and others for comfort (i.e. sucking a pacifier differently from a nipple or attempting to hold a bottle to suck it).

The next two substages (3 and 4), involve the infant’s responses to objects and people, called secondary circular reactions.  Reactions are no longer confined to the infant’s body and are now interactions between the baby and something else.

Substage Three:  Repetition (4th through 8th months)

During the next few months, the infant becomes more and more actively engaged in the outside world and takes delight in being able to make things happen by responding to people and objects. Babies try to continue any pleasing event. Repeated motion brings particular interest as the infant is able to bang two lids together or shake a rattle and laugh. Another example might be to clap their hands when a caregiver says “patty-cake.” Any sight of something delightful will trigger efforts for interaction.

Substage Four:  New Adaptations and Goal-Directed Behavior (8th through 12th months)

Now the infant becomes more deliberate and purposeful in responding to people and objects and can engage in behaviors that others perform and anticipate upcoming events. Babies may ask for help by fussing, pointing, or reaching up to accomplish tasks, and work hard to get what they want. Perhaps because of continued maturation of the prefrontal cortex, the infant becomes capable of having a thought and carrying out a planned, goal-directed activity such as seeking a toy that has rolled under the couch or indicating that they are hungry. The infant is coordinating both internal and external activities to achieve a planned goal and begins to get a sense of social understanding. Piaget believed that at about 8 months (during substage 4), babies first understood the concept of object permanence, which is the realization that objects or people continue to exist when they are no longer in sight.

The last two stages (5 and 6), called tertiary circular reactions , consist of actions (stage 5) and ideas (stage 6) where infants become more creative in their thinking.

Substage Five:  Active Experimentation of “Little Scientists” (12th through 18th months)

The toddler is considered a “little scientist” and begins exploring the world in a trial-and-error manner, using motor skills and planning abilities. For example, the child might throw their ball down the stairs to see what happens or delight in squeezing all of the toothpaste out of the tube. The toddler’s active engagement in experimentation helps them learn about their world. Gravity is learned by pouring water from a cup or pushing bowls from high chairs. The caregiver tries to help the child by picking it up again and placing it on the tray. And what happens? Another experiment! The child pushes it off the tray again causing it to fall and the caregiver to pick it up again! A closer examination of this stage causes us to really appreciate how much learning is going on at this time and how many things we come to take for granted must actually be learned. This is a wonderful and messy time of experimentation and most learning occurs by trial and error.

See how even babies think like little scientists in the selected clip from this Ted talk.

Substage Six:  Mental Representations (18th month to 2 years of age)

The child is now able to solve problems using mental strategies, to remember something heard days before and repeat it, to engage in pretend play, and to find objects that have been moved even when out of sight. Take, for instance, the child who is upstairs in a room with the door closed, supposedly taking a nap. The doorknob has a safety device on it that makes it impossible for the child to turn the knob. After trying several times to push the door or turn the doorknob, the child carries out a mental strategy to get the door opened – he knocks on the door! Obviously, this is a technique learned from the past experience of hearing a knock on the door and observing someone opening the door. The child is now better equipped with mental strategies for problem-solving. Part of this stage also involves learning to use language. This initial movement from the “hands-on” approach to knowing about the world to the more mental world of stage six marked the transition to preoperational thinking, which you’ll learn more about in a later module.

Development of Object Permanence

A critical milestone during the sensorimotor period is the development of object permanence. Introduced during substage 4 above, object permanence is the understanding that even if something is out of sight, it continues to exist. The infant is now capable of making attempts to retrieve the object. Piaget thought that, at about 8 months, babies first understand the concept of objective permanence, but some research has suggested that infants seem to be able to recognize that objects have permanence at much younger ages (even as young as 4 months of age). Other researchers, however, are not convinced (Mareschal & Kaufman, 2012). [1] It may be a matter of “grasping vs. mastering” the concept of objective permanence. Overall, we can expect children to grasp the concept that objects continue to exist even when they are not in sight by around 8 months old, but memory may play a factor in their consistency. Because toddlers (i.e., 12–24 months old) have mastered object permanence, they enjoy games like hide-and-seek, and they realize that when someone leaves the room they will come back (Loop, 2013). Toddlers also point to pictures in books and look in appropriate places when you ask them to find objects.

Although the styles and cinematography in this video are dated, the information is valuable in understanding how researchers, like Dr. Rene Baillargeon, study object permanence in young infants.

Learning and Memory Abilities in Infants

Memory is central to cognitive development. Our memories form the basis for our sense of self, guide our thoughts and decisions, influence our emotional reactions, and allow us to learn (Bauer, 2008) [2] .

It is thought that Piaget underestimated memory ability in infants (Schneider, 2015) [3] .

As mentioned when discussing the development of infant senses, within the first few weeks of birth, infants recognize their caregivers by face, voice, and smell. Sensory and caregiver memories are apparent in the first month, motor memories by 3 months, and then, at about 9 months, more complex memories including language (Mullally & Maguire, 2014) [4] . There is agreement that memory is fragile in the first months of life, but that improves with age. Repeated sensations and brain maturation are required in order to process and recall events (Bauer, 2008). Infants remember things that happened weeks and months ago (Mullally & Maguire, 2014), although they most likely will not remember it decades later. From the cognitive perspective, this has been explained by the idea that the lack of linguistic skills of babies and toddlers limit their ability to mentally represent events; thereby, reducing their ability to encode memory. Moreover, even if infants do form such early memories, older children and adults may not be able to access them because they may be employing very different, more linguistically based, retrieval cues than infants used when forming the memory. 

Watch this Ted talk from Alison Gopnik to hear about more research done on cognition in babies.

Language Development

Given the remarkable complexity of a language, one might expect that mastering a language would be an especially arduous task; indeed, for those of us trying to learn a second language as adults, this might seem to be true. However, young children master language very quickly with relative ease. B. F. Skinner (1957) proposed that language is learned through reinforcement. Noam Chomsky (1965) criticized this behaviorist approach, asserting instead that the mechanisms underlying language acquisition are biologically determined. The use of language develops in the absence of formal instruction and appears to follow a very similar pattern in children from vastly different cultures and backgrounds. It would seem, therefore, that we are born with a biological predisposition to acquire a language (Chomsky, 1965; Fernández & Cairns, 2011). Moreover, it appears that there is a critical period for language acquisition, such that this proficiency at acquiring language is maximal early in life; generally, as people age, the ease with which they acquire and master new languages diminishes (Johnson & Newport, 1989; Lenneberg, 1967; Singleton, 1995).

Children begin to learn about language from a very early age (Table 1). In fact, it appears that this is occurring even before we are born. Newborns show a preference for their mother’s voice and appear to be able to discriminate between the language spoken by their mother and other languages. Babies are also attuned to the languages being used around them and show preferences for videos of faces that are moving in synchrony with the audio of spoken language versus videos that do not synchronize with the audio (Blossom & Morgan, 2006; Pickens, 1994; Spelke & Cortelyou, 1981).

Each language has its own set of phonemes that are used to generate morphemes , words, and so on. Babies can discriminate among the sounds that make up a language (for example, they can tell the difference between the “s” in vision and the “ss” in fission); early on, they can differentiate between the sounds of all human languages, even those that do not occur in the languages that are used in their environments. However, by the time that they are about 1 year old, they can only discriminate among those phonemes that are used in the language or languages in their environments (Jensen, 2011; Werker & Lalonde, 1988; Werker & Tees, 1984).

This video explains some of the research surrounding language acquisition in babies, particularly those learning a second language.

Newborn Communication

Do newborns communicate? Certainly, they do. They do not, however, communicate with the use of language. Instead, they communicate their thoughts and needs with body posture (being relaxed or still), gestures, cries, and facial expressions. A person who spends adequate time with an infant can learn which cries indicate pain and which ones indicate hunger, discomfort, or frustration.

Intentional Vocalizations

Infants begin to vocalize and repeat vocalizations within the first couple of months of life. That gurgling, musical vocalization called cooing can serve as a source of entertainment to an infant who has been laid down for a nap or seated in a carrier on a car ride. Cooing serves as practice for vocalization. It also allows the infant to hear the sound of their own voice and try to repeat sounds that are entertaining. Infants also begin to learn the pace and pause of conversation as they alternate their vocalization with that of someone else and then take their turn again when the other person’s vocalization has stopped. Cooing initially involves making vowel sounds like “oooo.” Later, as the baby moves into babbling (see below), consonants are added to vocalizations such as “nananananana.”

Babbling and Gesturing

Between 6 and 9 months, infants begin making even more elaborate vocalizations that include the sounds required for any language. Guttural sounds, clicks, consonants, and vowel sounds stand ready to equip the child with the ability to repeat whatever sounds are characteristic of the language heard. These babies repeat certain syllables (ma-ma-ma, da-da-da, ba-ba-ba), a vocalization called babbling because of the way it sounds. Eventually, these sounds will no longer be used as the infant grows more accustomed to a particular language. Deaf babies also use gestures to communicate wants, reactions, and feelings. Because gesturing seems to be easier than vocalization for some toddlers, sign language is sometimes taught to enhance one’s ability to communicate by making use of the ease of gesturing. The rhythm and pattern of language are used when deaf babies sign just as when hearing babies babble.

At around ten months of age, infants can understand more than they can say. You may have experienced this phenomenon as well if you have ever tried to learn a second language. You may have been able to follow a conversation more easily than to contribute to it.

Holophrasic Speech

Children begin using their first words at about 12 or 13 months of age and may use partial words to convey thoughts at even younger ages. These one-word expressions are referred to as holophrasic speech ( holophrase ). For example, the child may say “ju” for the word “juice” and use this sound when referring to a bottle. The listener must interpret the meaning of the holophrase. When this is someone who has spent time with the child, interpretation is not too difficult. They know that “ju” means “juice” which means the baby wants some milk! But, someone who has not been around the child will have trouble knowing what is meant. Imagine the parent who exclaims to a friend, “Ezra’s talking all the time now!” The friend hears only “ju da ga” which, the parent explains, means “I want some milk when I go with Daddy.”

Underextension

A child who learns that a word stands for an object may initially think that the word can be used for only that particular object. Only the family’s Irish Setter is a “doggie.” This is referred to as underextension. More often, however, a child may think that a label applies to all objects that are similar to the original object. In overextension, all animals become “doggies,” for example.

First words and cultural influences

First words for English-speaking children tend to be nouns. The child labels objects such as a cup or a ball. In a verb-friendly language such as Chinese, however, children may learn more verbs. This may also be due to the different emphasis given to objects based on culture. Chinese children may be taught to notice action and relationship between objects while children from the United States may be taught to name an object and its qualities (color, texture, size, etc.). These differences can be seen when comparing interpretations of art by older students from China and the United States.

Vocabulary growth spurt

One-year-olds typically have a vocabulary of about 50 words. But by the time they become toddlers, they have a vocabulary of about 200 words and begin putting those words together in telegraphic speech (short phrases). This language growth spurt is called the  naming explosion because many early words are nouns (persons, places, or things).

Two-word sentences and telegraphic speech

Words are soon combined and 18-month-old toddlers can express themselves further by using phrases such as “baby bye-bye” or “doggie pretty.” Words needed to convey messages are used, but the articles and other parts of speech necessary for grammatical correctness are not yet included. These expressions sound like a telegraph (or perhaps a better analogy today would be that they read like a text message) where unnecessary words are not used. “Give baby ball” is used rather than “Give the baby the ball.” Or a text message of “Send money now!” rather than “Dear Mother. I really need some money to take care of my expenses.” You get the idea.

Child-directed speech

Why is a horse a “horsie”? Have you ever wondered why adults tend to use “baby talk” or that sing-song type of intonation and exaggeration used when talking to children? This represents a universal tendency and is known as child-directed speech or motherese or parentese. It involves exaggerating the vowel and consonant sounds, using a high-pitched voice, and delivering the phrase with great facial expression. Why is this done? It may be in order to clearly articulate the sounds of a word so that the child can hear the sounds involved. Or it may be because when this type of speech is used, the infant pays more attention to the speaker and this sets up a pattern of interaction in which the speaker and listener are in tune with one another. When I demonstrate this in class, the students certainly pay attention and look my way. Amazing! It also works in the college classroom!

This video examines new research on infant-directed speech.

Theories of Language Development

How is language learned? Each major theory of language development emphasizes different aspects of language learning: that infants’ brains are genetically attuned to language, that infants must be taught, and that infants’ social impulses foster language learning. The first two theories of language development represent two extremes in the level of interaction required for language to occur (Berk, 2007).

Chomsky and the language acquisition device

This theory posits that infants teach themselves and that language learning is genetically programmed. The view is known as nativism  and was advocated by Noam Chomsky, who suggested that infants are equipped with a neurological construct referred to as the language acquisition device (LAD) , which makes infants ready for language. The LAD allows children, as their brains develop, to derive the rules of grammar quickly and effectively from the speech they hear every day. Therefore, language develops as long as the infant is exposed to it. No teaching, training, or reinforcement is required for language to develop. Instead, language learning comes from a particular gene, brain maturation, and the overall human impulse to imitate.

Skinner and reinforcement

This theory is the opposite of Chomsky’s theory because it suggests that infants need to be taught language. This idea arises from behaviorism. Learning theorist, B. F. Skinner, suggested that language develops through the use of reinforcement. Sounds, words, gestures, and phrases are encouraged by following the behavior with attention, words of praise, treats, or anything that increases the likelihood that the behavior will be repeated. This repetition strengthens associations, so infants learn the language faster as parents speak to them often. For example, when a baby says “ma-ma,” the mother smiles and repeats the sound while showing the baby attention. So, “ma-ma” is repeated due to this reinforcement.

Social pragmatics

Another language theory emphasizes the child’s active engagement in learning the language out of a need to communicate. Social impulses foster infant language because humans are social beings and we must communicate because we are dependent on each other for survival. The child seeks information, memorizes terms, imitates the speech heard from others, and learns to conceptualize using words as language is acquired. Tomasello &  Herrmann (2010) argue that all human infants, as opposed to chimpanzees, seek to master words and grammar in order to join the social world  [5] Many would argue that all three of these theories (Chomsky’s argument for nativism, conditioning, and social pragmatics) are important for fostering the acquisition of language (Berger, 2004).

Moral Reasoning in Infants

The foundation of moral reasoning in infants.

The work of Lawrence Kohlberg was an important start to modern research on moral development and reasoning. However, Kohlberg relied on a specific method: he presented moral dilemmas and asked children and adults to explain what they would do and—more importantly—why they would act in that particular way. Kohlberg found that children tended to make choices based on avoiding punishment and gaining praise. But children are at a disadvantage compared to adults when they must rely on language to convey their inner thoughts and emotional reactions, so what they say may not adequately capture the complexity of their thinking.

Starting in the 1980s, developmental psychologists created new methods for studying the thought processes of children and infants long before they acquire language. One particularly effective method is to present children with puppet shows to grab their attention and then record nonverbal behaviors, such as looking and choosing, to identify children’s preferences or interests.

A research group at Yale University has been using the puppet show technique to study moral thinking of children for much of the past decade. What they have discovered has given us a glimpse of surprisingly complex thought processes that may serve as the foundation of moral reasoning.

EXPERIMENT 1: Do children prefer givers or takers?

In 2011, J. Kiley Hamlin and Karen Wynn put on puppet shows for very young children: 5-month-old infants. The infants watch a puppet bouncing a ball. We’ll call this puppet the “bouncer puppet.” Two other puppets stand at the back of the stage, one to left and the other to the right. After a few bounces, the ball gets away from the bouncer puppet and rolls to the side of the stage toward one of the other puppets. This puppet grabs the ball. The bouncer puppet turns toward the ball and opens its arms as if asking for the ball back.

This is where the puppet show gets interesting (for a young infant, anyway!).  Sometimes, the puppet with the ball rolls it back to the bouncer puppet. This is the “giver puppet” condition. Other times, the infant sees a different ending. As the bouncer puppet opens its arms to ask for the ball, the puppet with the ball turns and runs away with it. This is the “taker puppet” condition. Although the giver and taker puppets are two copies of the same animal doll, they are easily distinguished because they are wearing different colored shirts, and color is a quality that infants easily distinguish and remember. It looks like this:

Each infant sees both conditions: the giver condition and the taker condition. Just after the end of the second puppet show (i.e., the second condition), a new researcher, who doesn’t know which puppet was the giver and which was the taker, sits in front of the infant with the giver puppet in one hand and the taker puppet in the other. The 5-month-old infants are allowed to reach for a puppet. The one the child reaches out to touch is considered the preferred puppet.

Remember that Lawrence Kohlberg thought that children at this age—and, in fact, through 9 years of age—are primarily motivated to avoid punishment and seek rewards. Neither Kohlberg nor Carol Gilligan nor Jean Piaget was likely to predict that infants would develop preferences based on the type of behavior shown by other individuals.

Work It Out

The puppet show is over and the experimenter is holding the two dolls—the giver puppet and the taker puppet—in front of the infant. The reaching behavior of the infant is being videotaped for later analysis.

What do you think? Make a prediction about the results of this study—which should reflect your own theory of an infant’s ability to judge and care about the types of behavior others display. Do you think infants will choose the taker or the giver puppet? Do you expect the results to be significant?

INSTRUCTIONS: Adjust the pink bar on the left to show the percentage of infants who reached for the giver puppet. The yellow bar on the right will automatically adjust to make the total (sum of both bars) equal 100%.

https://s3-us-west-2.amazonaws.com/oerfiles/Psychology/interactives/moral_bars1.html

[reveal-answer q=”291461″]Show Answer[/reveal-answer] [hidden-answer a=”291461″] Here are the results from Experiment 1:

Experiment 1 suggests that 5-month-old infants are not just passive observers. They notice what others do and, if we are interpreting the results of experiments like this one correctly, they distinguish helpful behaviors (“prosocial behaviors”) from behaviors that hurt others (“antisocial behaviors”). But they do more that that. They are attracted to those who are acting in a prosocial way, and they reject those who act in an antisocial way.

These researchers also tested infants who were only 3-months old. These infants were so immature that they did not yet have good control of their arms, so the experimenter could not use “reaching for one of the puppets” as the dependent variable, as they did with the 5-month-olds. Three-month-old infants can control where they look quite well, and previous research has indicated that very young infants will look longer at objects they want. The researchers showed these very young infants the same puppet shows that were described above and then, during the choice phase, they recorded which puppet (giver or taker) the 3-month-olds looked at longer. The results were very similar to those found with the 5-month-olds. A strong majority of younger infants (92%) looked longer at the giver puppet than the taker puppet. [/hidden-answer]

But this isn’t the end of the story…

EXPERIMENT 2: Do infants judge others based on their behavior?

In the research world, the early attempts to study something, when the researchers work to develop a solid and reliable research procedure, is often the most challenging time. Once the researcher works through initial problems and issues and begins to get consistent results, they can gain a deeper understanding by adding new variables or testing different groups of subjects (e.g., older children or children with some interesting psychological characteristics).

The study you just read about is an example of a simple, basic study. The researchers found that infants preferred puppets that help another puppet (the puppet in the giver condition) over puppets that are not nice to another puppet (the puppet in the taker condition). A common sense interpretation of this simple result is that infants like nice behavior and they dislike hurtful behavior. And perhaps that is as complicated as an 8-month-old infant’s thoughts can be. But maybe not.

Dr. Hamlin and her colleagues wondered if infants might consider more factors when judging an event. Adults generally prefer situations where good things happen to someone rather than something harmful. However, when adults see someone do something bad, they may find satisfaction in seeing that person punished by having something bad happen to him or her. In a nutshell: good things should happen to good people and bad things should happen to bad people. This is what is called “just world” thinking, where people get what they deserve.

In the study we will call Experiment 2, Hamlin’s team tested 8-month-old infants and repeated the procedures from Experiment 1 with a major addition. In Experiment 1 (described above), the puppet bouncing the ball was a neutral character, neither good nor bad. In Experiment 2, the infants saw 2 different shows. First, they saw the bouncer puppet either helping or hindering another puppet. Then, they watched the same ball-bouncing puppet show. Here is what happened:

• Puppet Show #1:  A puppet is trying to open a box, but cannot quite succeed. Two puppets stand in the background. For some infants, as the first puppet struggles to open the box, one of the puppets in the back comes forward and helps to open the box. This is the helper puppet. For other children, as the first puppet struggles, a puppet comes from the back and jumps on the box, slamming it shut. This is the hinderer puppet. Each infant sees only a helper or a hinderer—not both. Here is a video showing the helper puppet situation:

https://s3-us-west-2.amazonaws.com/oerfiles/Psychology/Boxlidhelper.mp4

• Puppet Show #2: Just after the infants have watched the first show, the second puppet show begins. This is the show that you read about in Experiment 1. The only thing that is new is that the bouncer puppet, the one that loses the ball, is either the helper puppet from Puppet Show #1 or the hinderer puppet from Puppet Show #1. Each infant sees this puppet lose the ball to a giver, who returns the ball, and to a taker, who runs off with the ball.

This video demonstrates show #2.  The elephant in the yellow shirt from the first show is now bouncing a ball. After dropping the ball, the moose in the green shirt gives it back to him, while the moose in the red shirt takes it away.

So far we have concluded that even young babies prefer the “nice” puppet and show a preference for a puppet who helps another puppet. But this only happened when the bouncer puppet was the helper from the first puppet show. What if, instead of the nice elephant in the yellow shirt bouncing the ball, the elephant in the red shirt (the one who jumped on the duck’s box, remember?) was the one bouncing the ball? Imagine the same scenario: the mean elephant in the red shirt is bouncing the ball, he drops it, and the moose in the green shirt gives it to him or the moose in the red shirt takes it away.

So now things are getting interesting, right? Do 8-month old infants understand the concepts of revenge or justice? We must always be careful when labeling behaviors of children (or animals) with characteristics we use for human adults. In the description above, we have talked of “nice puppets” and “mean puppets” and used other loaded terms. It is tempting to interpret the choices of the 8-month-olds as a kind of revenge motive: the bad guy gets its just desserts (the hinderer puppet has its ball stolen) and the good guy gets its just reward (the helper puppet is itself helped by the giver). Maybe that is what is going on, but we encourage you to consider these very sophisticated types of thinking as merely one hypothesis. Remember the facts—what did the puppets do and what choices did the infants make?—without committing yourself to the adult-level interpretation.

The researchers believe that this type of thinking, which is remarkably sophisticated, takes some cognitive development. They tested 5-month-olds using the same procedures, and the results with these younger infants were different. The 5-month-olds showed an overwhelming preference for the giver puppets, regardless of who was bouncing the ball. Maybe it is too complex for them to understand that the bouncer puppet in the second show was the same puppet from the first show. Or perhaps their memory processes are too fragile to hold onto information for that length of time. Maybe the revenge motive is too advanced. Or maybe something else is going on. What is clear is that 5-month-olds and 8-month-olds respond differently to the situations tested in the second experiment.

EXPERIMENT 3: Do infants judge others based on their preferences?

Across the first two experiments, infants appear to prefer puppets (and, by extension, maybe people, as well) that are helpful over those that are not helpful. Experiment 2 complicated our story a bit, but it still appears that prosocial behavior is attractive to infants and antisocial behavior is unattractive. But another experiment, again using the bouncing ball show, suggests that infants as young as 8-months of age may have some other motives that are less altruistic than the first two experiments indicate.

In a study by Hamlin, Mahanjan, Liberman, and Wynn from 2013, 9-month-old infants watched the bouncing ball show, but with a new twist.

At the beginning of the experiment—Phase 1—the infants were given a choice between graham crackers and green beans. The experimenters determined which food the infant preferred.

Then, in Phase 2, the infants watched a puppet make the same choice. For half of the infants, the puppet chose the same food that they preferred, saying “Mmmm, yum! I like ___(graham crackers or green beans)!” and saying “Eww, yuck! I don’t like _____ (graham crackers or green beans!”  This was called the SIMILAR condition because the puppet was similar to the child in its food preference. For the other half of the infants, the puppet chose the other food, choosing graham crackers if the infant preferred green beans and preferring green beans if the infant liked graham crackers. This was the DISSIMILAR condition.

Why did the experimenters do this? They wanted to know if young children form in-groups and out-groups by perceiving some people as being like them and other people as being unlike them. The experimenters noted in their research introduction that we (adults) are influenced by our perception that others are similar to us or not like us. We tend to project positive qualities—being trustworthy, intelligent, kind—on people we perceive as similar to ourselves, and people we see as unlike us are seen as having negative qualities—being relatively untrustworthy, unintelligent, and unkind. [6]

Of course, there is a big difference between claiming that adults use similarity to make judgments about others and saying that infants less than a year of age do the same thing. However, the researchers note that some recent research has suggested that infants less than a year old are more likely to develop peer friendships with other infants who “share their own food, clothing, or toy preferences” compared to those who don’t.

So, back to the experiment. In Phase 3, the infants either saw a similar puppet (one that chose the food the baby preferred) or a dissimilar puppet (one that chose the food the baby did not prefer) bouncing the ball. As in the other experiments, the ball got away from the bouncer and rolled to the back of the stage. In one instance, the giver puppet returned the ball and, in the other instance, the taker puppet ran away with the ball. Finally, in Phase 4, the 9-month-old baby was shown the giver and taker puppet and the experimenters recorded which of the two puppets the baby preferred (reached out to touch). This video shows the dog in the light blue shirt giving the ball back to the red bunny that preferred graham crackers.

Here is a summary of the four phases in Experiment 3:

• Phase 1: The infant chooses graham crackers or green beans.
• Similar condition: The bouncer chooses the same food that the infant chose.
• Dissimilar condition: The bouncer chooses the food that the infant did not choose.
• Remember that each child sees both the Giver and Taker shows.
• Phase 4: This is the same choice—Giver or Taker—that was the final phase in the other two experiments

Now make predictions for the results. Here is a matrix picture of the design of the experiment:

INSTRUCTIONS: Adjust bars A and C to make your predictions. Bar A represents the “nice” puppet who gave the ball to the bouncer puppet that liked the same food as the child, while bar B represents the “mean” puppet who took the ball away from the bouncer puppet who liked the same food as the child. Bar C represents the “nice” puppet who gave the ball back to the puppet who did not like the same food as the child, and bar D represents the puppet who took the ball away from the puppet who did not like the same food.

As before, move the bars on the left to indicate the percentage of infants preferring the giver puppet in the similar condition (purple bars) and in the dissimilar condition (green bars). The bars on the right will adjust to make the total in each of the similarity conditions equal 100%.

After you have recorded your predictions, click the “Show Answer” link to see the results from the experiment.

https://s3-us-west-2.amazonaws.com/oerfiles/Psychology/interactives/moral_bars2.html

[reveal-answer q=”291462″]Show Answer[/reveal-answer] [hidden-answer a=”291462″] Here are the results from Experiment 3:

These results are similar to those for the 8-month-olds in the previous experiment. But remember that, in this experiment, the variable that distinguishes the two bouncer puppets was a food choice, not the prosocial or antisocial behavior in Experiment 2. If we take the results from Experiments 2 and 3 together, the results here suggest that the similar puppet is being treated as if it is nice or good. Puppets that treat this similar puppet in a nice way are preferred. Conversely, the dissimilar puppets are treated as if they have done something negative and puppets that treat these dissimilar puppets badly are preferred.

[/hidden-answer]

The experimenters also tested an older group of babies that were 14-months-old. The results for these older babies were consistent with the 9-month-old and, if anything, the effects were stronger. Their results showed that all infants preferred when the giver puppet was nice to the puppet that was similar to them and all infants preferred when puppets were mean to the puppet that was dissimilar to them.

CONCLUSIONS

This exercise started with a reminder that Lawrence Kohlberg found that children went through a long developmental process in their moral reasoning. Based on children’s reasoning aloud about moral dilemmas, Kohlberg concluded that children younger than about 8 or 9 years of age make moral decisions based on avoiding punishment and receiving praise. Neither his research nor that of most others in the 1970s and 1980s suggested that young children would use multiple sources of information and judgments about the meaning of behaviors in their thinking about what sorts of behaviors are better or worse.

If Dr. Hamlin and her colleagues are right, then infants are much more sophisticated and complex in their thinking about the world than these earlier researchers thought. In Dr. Hamlin’s view, infants like good things to happen to good puppets and people, and bad things to happen to bad puppets and people. Experiment 3 suggests that they make judgments about more than helping and harming behavior. They prefer others who are like them (green beans vs. graham crackers) and they don’t mind if others who are not like them have unpleasant experiences.

The research we have been reviewing is just part of an impressive set of research on infant thinking. The ideas that the researchers have developed are intriguing and they are consistent with the modern view of the infant as an active, creative thinker. At the same time, remember that science doesn’t rest on an early set of explanations based on a small set of complicated experiments. Science pushes beyond what we currently know and believe. This starts with curiosity on your part. Are the experimenters correct in interpreting reaching behavior as showing a preference or is something else going on? Do infants really prefer prosocial behaviors to antisocial behaviors, or is there some other explanation for their preferences? How else could we test the moral judgments of infants without using puppet shows? The next generation of creative scientists will push beyond what we know now, with new research methods and new ideas about the mind.

We’ll give Dr. Hamlin the last word. Here is part of her conclusion section from an article that summarizes some of the research we have been studying: “In sum, recent developmental research supports the claim that at least some aspects of human morality are innate…Indeed, these early tendencies are far from shallow, mechanical predispositions to behave well or knee-jerk reactions to particular states of the world. Infant moral inclinations are sophisticated, flexible, and surprisingly consistent with adults’ moral inclinations, incorporating aspects of moral goodness, evaluation, and retaliation.“ (Hamlin, 2013, p. 191)

• Mareshcal, D. & Kauffman, J. (2012). Object Permanence in infancy: Revisiting Baillargeon's drawbridge study. In Alan M. Slaster & Paul C. Quinn (Eds.), Developmental Psychology: Revisiting the classic studies. Thousand Oaks, CA: Sage. ↵
• Bauer PJ, Pathman T. Memory and Early Brain Development. In: Tremblay RE, Boivin M, Peters RDeV, eds. Paus T, topic ed. Encyclopedia on Early Childhood Development [online]. http://www.child-encyclopedia.com/brain/according-experts/memory-and-early-brain-development. Published December 2008. Accessed March 2, 2019. ↵
• Schneider, Wolfgang. (2015). This belief came in part from findings that adults rarely recall personal events from before the age of 3 years (a phenomenon known as  infantile or childhood amnesia ). However, research with infants and young children has made it clear that they can and do form memories of events. Memory development from early childhood through emerging adulthood. Switzerland: Spring International. doi: 10.1007/978-3-319-09611-7. ↵
• Mullally, Sinead L. & Maguire, Eleanor. A. (2014). Learning to remember: The early ontogeny of episodic memory. Developmental Cognitive Neuroscience, 9(13), 12-29. doi: 10.1016/j.dcn.2013.12.006 ↵
• Tomasello, M. & Hermann, E. (2010). Ape and human cognition. Current Directions in Psychological Science, 19(1), 3-8. ↵
• The experimenters support these claims by citing the following studies: (1) DeBruine, L.M. Facial resemblance enhances trust: Proceedings of the Royal Society of London B, 2002, 269: 1307-1312. (2) Brewer, M.B. In-group bias in the minimal intergroup situation: A cognitive-motivational analysis. Psychological Bulletin, 1979, 86: 307-324. (3) Doise, W., Cspely, G., Dann, and others. An experimental investigation into the formation of intergroup representation. European Journal of Social Psychology, 1972, 2: 202-204. ↵

Lifespan Development Copyright © 2020 by Lumen Learning 2019 is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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The physical environment and child development: An international review

Kim t. ferguson.

a Sarah Lawrence College

Rochelle C. Cassells

b Department of Human Development, Cornell University

Jack W. MacAllister

Gary w. evans.

c Department of Environmental Design and Analysis, Cornell University

d Bronfenbrenner Center for Translational Research, Cornell University

A growing body of research in the United States and Western Europe documents significant effects of the physical environment (toxins, pollutants, noise, crowding, chaos, housing, school and neighborhood quality) on children and adolescents’ cognitive and socioemotional development. Much less is known about these relations in other contexts, particularly the global South. We thus briefly review the evidence for relations between child development and the physical environment in Western contexts, and discuss some of the known mechanisms behind these relations. We then provide a more extensive review of the research to date outside of Western contexts, with a specific emphasis on research in the global South. Where the research is limited, we highlight relevant data documenting the physical environment conditions experienced by children, and make recommendations for future work. In these recommendations, we highlight the limitations of employing research methodologies developed in Western contexts ( Ferguson & Lee, 2013 ). Finally, we propose a holistic, multidisciplinary and multilevel approach based on Bronfenbrenner’s (1979) bioecological model to better understand and reduce the aversive effects of multiple environmental risk factors on the cognitive and socioemotional development of children across the globe.

The majority of the world’s children live in the global South (countries with a low to medium Human Development Index score, including Africa, Central and Latin America, and most of Asia), yet nearly all of the research on relations between the physical environments experienced by children and their cognitive and socioemotional development has taken place within North America and Western Europe. The purpose of this review is to call attention to this important gap in the literature and to introduce readers to emerging scholarship on children’s environments in the global South. We do not cover work on the physical environment and children’s physical health because this literature is extensive (cf., Wigle, 2003 ). We do, however, discuss physiological indicators of stress in explaining relations between components of the physical environment (e.g., crowding, noise) and children’s development.

We organize our review into a discussion of the impacts of toxins and pollutants (heavy metals, pesticides, air and water pollution), noise, crowding, chaos, housing quality, school and childcare quality, and neighborhood quality on the cognitive and socioemotional development of children and adolescents across the globe. For each of these commonly studied physical environment factors, we briefly review what is currently known in Western (North American and Western European) contexts and, where appropriate, discuss some of the known mechanisms linking each factor and children’s development. We also identify when the evidence is especially strong for particular influences. We provide a more extensive review of the research to date outside of Western contexts, with a specific emphasis on research in the global South. As we do so, we discuss the strength of the evidence for each influencing factor and, where there are gaps in the extant research, we briefly discuss what we do know (including available statistics) and make recommendations for future work. In these recommendations, we pay particular attention to the limitations of employing the same research methodologies and predicting similar results in previously under-studied contexts ( Ferguson & Lee, 2013 ; Nsamenang, 1992 ; 2004 ). We close with a call for a holistic, multidisciplinary and multilevel approach to investigate the impacts of the physical environment on child and adolescent development that employs an extension of Bronfenbrenner’s bioecological model ( Bronfenbrenner, 1979 ; Bronfenbrenner & Evans, 2000 ; Ferguson & Lee, 2013 ; Ferguson, Kim, Dunn, & Evans, 2009 ) as a theoretical framework.

Toxins and pollutants

Needleman et al. (1979) documented the impacts of lead exposure on young (grade school) children’s IQ and externalizing behaviors. Since then, many studies have shown that lead significantly impacts the cognitive functioning of children and adolescents in the United States and Western Europe, even when controlling for socioeconomic status (SES) and other confounding factors ( Evans, 2006 ; Hubbs-Tait, Nation, Krebs, & Bellinger, 2005 ; Koger, Schetteler, & Weiss, 2005 , Surkan et al., 2007 ; Wigle, 2003 ). More recently, a significant body of research has documented the effects of prenatal and childhood exposure to lead on children’s current and prospective developmental functioning in middle-income, newly industrial countries such as China ( Wang, Xu, Zhang, & Wang, 1989 ; Shen et al., 1998 ; Tang et al., 2008 ), India ( Ahamad et al., 2007 ; Patel, Mamtani, Thakre, & Kulkarni, 2006 ), the Philippines ( Solon et al., 2008 ) and Malaysia ( Zailina, Junidah, Josephine, & Jamal, 2008 ). Similar impacts have been documented in Egypt ( Mostafa, El-Shahawi, & Mokhtar, 2009 ), Mexico ( Acosta-Saavegra, 2011 ; Hu et al., 2006 ; Kordas et al., 2006 ), Peru ( Vega-Dienstmaier et al., 2006 ) and Bolivia ( Ruiz-Castell et al., 2012 ). Importantly, lead levels in these and other countries in the global South are still high and largely unregulated ( Karrari et al., 2012 ; Shen et al., 1998 ; Tong, von Schirnding, & Prapamontol, 2000 ; Walker et al., 2007 ). In fact, it is estimated that around 40% of children living in economically developing countries have elevated blood lead levels ( Walker et al., 2007 ). In addition, some of the most recent work across the globe has found that even very low levels of lead exposure can be toxic to infants and young children ( Canfield et al., 2003 ; Lanphear, Dietrich, Auinger, & Cox, 2000 ; Lanphear et al., 2005 ; Patel et al., 2006 ; Ruiz-Castell et al., 2012 ; Zailina et al., 2008 ).

An important characteristic of many toxins is that even after emissions are eliminated (e.g., removal of lead from gasoline and paint, the banning of the pesticide dichlorodiphenyltrichloroethane – DDT – in North America), they remain in the ecosystem for a very long time ( Meyer, Brown, & Falk, 2008 ). There are several pathways that enable this to occur. Heavy metals settle into the ground, and so lead is still found in the soil and in older houses that were painted prior to the banning of lead over three decades ago in the US. Lead used to be incorporated in plumbing (e.g., sodder) and thus can potentially leach into water supplies. Many toys and other common household products used to be made with lead, a practice that unfortunately continues today in China, for example ( Meyer et al., 2008 ). Another pathway that is perhaps more insidious is the cross-generational transmission of toxins. Some toxins are lipophilic, which means they can be stored in body fat. Thus prior exposure to some toxins, even preconception, can eventually affect the developing organism ( Hubbs-Tait et al., 2005 ; Koger et al., 2005 ). Finally, even when children themselves are not exposed to toxins, they may be susceptible to indirect exposure via parental exposures (Bouchard et al., 2011). A common example of this is from agricultural workers who absorb pesticides into their skin and/or their clothes ( Koger et al., 2005 ). Tragically, child laborers in many parts of the world remain in direct contact with toxins in agricultural, construction, and manufacturing sectors.

Numerous studies in North America document a dose-response function between body lead level burdens and IQ reductions. These findings have been replicated and demonstrated in prospective research designs, and hold true even when statistical controls rule out alternative explanations such as social class ( Evans, 2006 ; Hubbs-Tait et al., 2005 ; Koger et al., 2005 ). For example, Canfield et al. (2003) found that, after controlling for socioeconomic status and other demographic variables, three- to five-year-olds’ blood lead levels were significantly negatively associated with IQ, even at levels of exposure below the US regulated 10 μg/dl level. Teachers also report more attentional problems among children who have been exposed to lead ( Evans, 2006 ), and at least one North American study uncovered lead related deficits in attention, reaction time, and visual-motor integration among elementary school aged children ( Chiodo et al., 2004 ).

Estimates of developmental impacts of toxins such as lead may underestimate effects because of genetic differences in vulnerability. As an illustration, Nigg et al. (2008) showed that blood lead levels among 8- to 17-year-old American children were weakly associated overall with hyperactivity and impulsivity. However, these symptoms were significantly more accentuated in the subset of youth with abnormalities in a catecholamine receptor gene.

In the global South, most of the work to date has considered the impacts of lead on general cognitive functioning. Mostafa et al. (2009) showed that nearly half (43%) of a middle-class sample of 6- to 12-year-olds in Cairo had blood lead levels at or above the US Centers for Disease Control and Prevention limit of 10 μg/dl. A large proportion (37%) of these children were diagnosed with cognitive dysfunction. The most significant independent predictor of cognitive dysfunction was a blood lead level at or above 10 μg/dl. Similar to research in other contexts, a 1-μg/dl increase in blood lead level was associated with a two-point decline in IQ. Similarly, in a study of 6.5- to 8.5-year-old urban Malaysian children, Zailina et al. (2008) found that blood lead levels, statistically controlling for parents’ education, household income, and other family demographic factors, predicted children’s cognitive functioning. Studies in China across a wide age range have documented similar effects (Ling et al., 1989; Shen et al., 1998 ; Tang et al., 2012 ), as have recent studies of 6- to 8.5-year-old children in Peru (Vega-Dienstmaier et al., 2005) and Ecuador ( Counter, Buchanan, & Ortega, 2008 ).

The evidence for long-term effects of lead on children’s cognitive functioning following prenatal exposure is equally strong. Most recently, Yorifuji et al. (2011) found that, after controlling for SES and other potential covariates, 7- and 14-year-old children living in the Faroe Islands exposed to high levels of lead had deficits in short-term memory and attention compared to children exposed to lower levels of lead. Hu et al. (2006) found that maternal blood lead levels in the first trimester, but not in the second or third trimester, predicted 12- and 24-month-old Mexican infants’ general cognitive functioning (Mental Development Index, MDI). These effects were large; a 1-SD increase in first-trimester maternal plasma lead level was associated with a 3.5-point decrease on the MDI. Shen et al. (1998) similarly found that, after statistically controlling for confounding factors such as family SES and parental exposure to lead at work, 3-, 6-, and 12-month-old Shanghai infants with high umbilical cord lead levels received significantly lower MDI scores than those with lower lead levels. Current blood lead levels were not associated with MDI scores. In contrast, Solon et al. (2008) found that 6- to 30-month-old Filipino infants’ current blood lead levels significantly predicted their MDI scores. And Patel and colleagues (2006) found that the cord blood lead levels of neonates living in Nagpur, India, significantly predicted autonomic stability and abnormal reflexes. In addition, amongst infants with cord blood lead levels of 5–10 μg/dL, lead levels significantly predicted arousal state regulation, motor functioning and autonomic stability. These findings suggest that lead exposure has important effects on early motor functioning, even at very low levels.

Much less work on behavioral toxins has examined potential adverse socioemotional consequences. However, in Needleman et al.’s (1979) classic Boston school children study of lead and IQ, teachers, blind to the pupil’s lead dentine levels, rated children with higher lead burdens with more overt classroom behaviors indicative of behavioral problems such as inhibitory control. Eleven years later, these same children had higher rates of juvenile delinquency ( Needleman, Schell, Bellinger, Leviton, & Allred, 1990 ). Several other studies have shown linkages between early lead exposure and impulsivity, aggression, and hyperactivity in children ( Chandramouli, Steer, Ellis, & Emond, 2009 ; Chiodo et al., 2004 ; Evans, 2006 ; Hubbs-Tait et al., 2005 ).

The research on associations between lead exposure and children’s socioemotional functioning outside of the Western world is even more limited. However, in an early study of the impacts of prenatal lead exposure on both cognitive and socioemotional functioning at ages 2, 4 and 7 years in Kosovo, Factor-Livak et al. (1999) found that children’s behavior problems were associated with blood lead levels. Similarly, Bao et al. (2009) found that levels of lead and zinc in 7- to 16-year-old Chinese children’s hair samples predicted their behavioral functioning. And, in an intervention study in which 6- to 8-year-old children living close to a metal foundry in Torreón, Mexico, were given iron, zinc, both or placebo nutrition supplements over a period of 6 months, Kordas et al. (2006) found that blood lead levels were positively associated with passive off-task behaviors within classroom settings and negatively associated with activity levels during recess.

The impacts of exposure to mercury on children’s cognitive functioning are well documented. Low-level maternal mercury exposure damages infant sensorimotor functioning ( Mckeown-Eyssen, Ruedy, & Neims, 1983 ) and 6-year-old children’s IQ scores and language development ( Kjellstrom, Kennedy, Wallis, & Mantell, 1989 ). In addition, high-level maternal mercury exposure in Japan ( Matsumoto, Koya, & Takeuchi, 1965 ; Takeuchi, 1968 ) and Iraq ( Cox et al., 1989 ; Cox, Marsh, Myers, & Clarkson, 1995 ; Marsh et al., 1980 ) has been reported to adversely affect cognitive and physical prenatal and neonatal development. Two major longitudinal projects, one in the Seychelles (e.g., Myers et al., 2009 ; Stokes-Riner et al., 2011 ) and one in the Faroe Islands (e.g., Debes et al., 2006 ), have documented the adverse impacts of prenatal exposure to mercury from maternal consumption of seafood on young children’s cognitive functioning. Little work has documented impacts on socioemotional functioning, suggesting that further work in this area is needed.

In the Seychelles Child Development Study, maternal and child methylmercury (MeHg) levels, children’s cognitive and behavioral development, and various demographic factors have been assessed at the ages of 6, 19, 29, 66 and 107 months ( Myers et al., 2009 ) following an assessment of prenatal MeHg exposure. In the Faroe Islands study, postnatal MeHg exposure and children’s cognitive and behavioral functioning have been measured at ages 1, 7 and 14 years ( Debes et al., 2006 ), following an assessment of prenatal levels. In both studies, significant relations between prenatal and current MeHg and children’s early motor development and later cognitive functioning have been found, although the results are more consistent in the Faroe Islands study ( Myers et al., 2009 ; Stokes-Riner et al., 2011 ). These differences may have resulted from differential sources of MeHg (primarily fish in the Seychelles; primarily pilot whale meat in the Faroe Islands), as well as lower levels of aquatic food consumption in the Seychelles. Nevertheless, together these projects suggest that young children’s motor and cognitive development, and language, attention and memory in particular, are compromised following prenatal exposure to methylmercury.

Polychlorinated biphenyls (PCBs)

Prenatal exposure to polychlorinated biphenyls (PCBs), which are used in the manufacture of vinyl and other plastic compounds, has been linked with children’s cognitive and socioemotional functioning ( Evans, 2006 ; Lai et al., 2002 ; Ribas-Fito, Sala, Kogevinas, & Sunyer, 2001 ; Williams & Ross, 2007 ). In contrast, postnatal exposure appears to have few effects, except in the case of severe poisoning ( Ribas-Fito et al., 2001 ). These compounds have been banned in most high-income countries, but they continue to persist in environments across the globe, particularly as they tend to bioaccumulate in fish and other animals ( Faroon, Keith, Smith-Simon, & De Rosa, 2003 ; WHO, 2010 ).

A series of studies at two different American sites indicate that prenatal PCB exposure due to fish ingestion from polluted lakes has consistent adverse effects on neonatal developmental status (especially hyporesponsiveness) and memory among preschool and elementary school aged children ( Evans, 2006 ). In a more recent set of studies among Native American adolescents, Newman and colleagues (2006 ; 2009 ) found that PCB body burden was associated with memory impairments and poorer comprehension/reasoning. This replicates some prior work with preadolescents ( Evans, 2006 ). An important and sobering aspect of these recent data is that, although indigenous populations in both the global North and South are frequently exposed to higher levels of toxins than are other populations, the Native American youths’ levels of PCBs were well within the “normal” range found in American children. Most research on PCBs and development has focused on highly exposed populations.

No known research has investigated the impacts of PCB exposure on the cognitive functioning of children living in the global South, and in fact levels of exposure are also largely unknown ( Faroon et al., 2003 ; WHO, 2010 ). However, presumably the effects would be consistent with those reported in other contexts. This was found to be the case in a longitudinal study assessing Taiwanese children’s cognitive and behavioral development every year through age 12 following prenatal exposure to PCBs in contaminated cooking oil ( Lai et al., 2002 ). In comparison to matched unexposed children, children exposed to PCBs had long-term deficits in IQ.

Much less is known about PCB exposure and various aspects of socioemotional development. There may be problems with executive functioning such as attentional control ( Evans, 2006 ; Hubbs-Tait et al., 2005 ; Koger et al., 2005 ). And Lai et al. (2002) found that Taiwanese children exposed to high levels of PCBs prenatally exhibited a greater number of externalizing and internalizing symptoms than did matched unexposed children.

One other developmental aspect of toxin exposure and children’s maturation worth mentioning is that lower SES contexts appear to accentuate the harmful impacts of toxins on children’s development ( Evans, 2006 ). This might occur for several reasons, including chronic stress, levels of cognitive stimulation in the home, co-occurrence of other toxin exposures, co-occurrence of other risk factors, and, for older children, poorer quality school environments.

Research on the developmental impacts of direct residential pesticide exposure or indirect prenatal or occupational exposure (on the skin or clothing of exposed caregivers) is somewhat limited. However, there is an extensive research literature documenting severe impacts of pesticide exposure on both rats and in vitro models of the mammalian brain (see, e.g., Aldrige, Meyer, Seidler, & Slotkin, 2005 ; Jameson, Seidler, & Slotkin, 2007 ). Since pesticides are neurotoxic agents, they may well have serious effects on the developing brain. Indeed, in a recent review, Jurewicz and Hanke (2008) conclude that there is good evidence for the impact of various pesticides on motor functioning (abnormal reflexes) in the newborn human and both motor and cognitive functioning (particularly reaction times, attention and short-term memory) on children. We also know that the developing fetus and young children have lower levels of the detoxifying enzymes that may deactivate organophosphate compounds in adults ( Furlong et al., 2006 ). This suggests that the effects of agricultural pesticides on children may be particularly problematic.

Dichlorodiphenyltrichloroethane (DDT) and related organochlorine compounds used as pesticides have been largely phased out in the US and Europe ( Rohlman et al., 2005 ). Thus their impacts on children’s developmental functioning in these contexts are understudied. However, a longitudinal study in the early 1990s in the United States found that prenatal dichlorodiphenyldichloroethylene (DDE) exposure impacted motor functioning at 18 and 24 months, but did not impact cognitive development at ages 3, 4 and 5 years ( Jurewicz & Hanke, 2008 ). Two more recent studies in Spain ( Ribas-Fito et al., 2003 ; 2006 ) and one in the United States ( Eskenazi et al., 2006 ), however, using similar assessment tools, did find significant relationships between cord blood and maternal serum levels of DDE, DDT and related compounds on the cognitive and psychomotor functioning of both infants and young children.

More contemporary organophosphate pesticides may similarly impact reflexes in infants ( Jurewicz & Hanke, 2008 ), reaction times in early childhood ( Rohlman et al., 2005 ), and infant and early childhood psychomotor development ( Jurewicz & Hanke, 2008 ; Rauh et al., 2006 ; Ruckart, Kakolewski, Bove, & Kaye, 2004 ). There is also some evidence for effects on specific cognitive skills, particularly short-term memory and attention ( Jurewicz & Hanke, 2008 ; Lizardi, O’Rourke, & Morris, 2008 ; Rauh et al., 2006 ; Ruckart et al., 2004 ). In addition, these effects appear to persist over time: Rauh et al. (2006) found that low-income, urban minority children in New York City who were exposed to high levels of the insecticide chlorpyrifos were more likely than other children to have delays in their overall cognitive and motor development at 12, 24 and 36 months, and were also more likely to exhibit attention problems.

DDT and DDE are currently commonly used in the global South ( Jurewicz & Hankel, 2008 ; Mishra & Sharma, 2011 ), yet there is almost no research documenting the impacts of these compounds on children’s developmental functioning. However, a longitudinal study of infant cognitive and psychomotor functioning following prenatal exposure to DDE in Mexico found that maternal serum levels during the first trimester were negatively associated with infants’ motor development at 1, 3, 6 and 12 months of age (Torres-Sanchez et al., 2007). Similarly, Grandjean et al. (2006) and Harari et al. (2010) found that prenatal exposure to pesticides adversely impacted Ecuadorian children’s cognitive functioning at ages 6–9 years. Children’s current exposure was negatively associated with reaction times, but not with other cognitive measures. Likewise, Guilette et al. (1998) found that Mexican 4- and 5-year-olds’ prenatal and current exposure to pesticides delayed their motor development and some aspects of cognitive functioning. In a study using a similar design, comparing children living in rural areas with high pesticide use to those residing in low pesticide use areas, 4- to 5-year-old Indian children showed a similar profile (Kuruganti, 2005). And Rodríguez (2012) found that 7- to 9-year-old children of Nicaraguan agricultural workers who were exposed to a variety of pesticides prenatally had deficits in working memory, verbal comprehension, and overall IQ. Eckerman et al. (2007) demonstrated similar impacts on 10- to 18-year-old Brazilian children’s memory and attention resulting from current exposure. Thus there is some evidence that prenatal exposure may be particularly problematic, but that later exposure may also impact some aspects of children’s cognitive development. In addition, there is good evidence for high levels of prenatal and childhood exposure to both organochlorine and organophosphate compounds in low- and middle-income countries, including India ( Mathews, Reis, & Iacopino, 2003 ; Mishra & Sharma, 2011 ), Kazakhstan ( Zetterström, 2003 ), Ghana ( Mull & Kirkhorn, 2005 ), Nigeria ( Okafor, 2010 ) and Egypt ( Kishk, Gaber, & Abd-Allah, 2004 ). In Ecuador, Corriols and Aragón (2010) estimated that there have been 18,516 cases of acute pesticide poisonings between 1995 and 2006 among children aged 5–14 years, based on the 2069 reported cases. Many of these were due to occupational exposure, which is in fact a primary mode of exposure for young children working in agricultural settings in the global South ( Dorman, 2008 ).

The research documenting effects of pesticide exposure on children’s socioemotional development is limited, and the findings are mixed ( Ruckart et al., 2004 ). Rodríguez (2012) , however, found that ADHD symptoms were more common amongst pesticide-exposed girls, but not boys, in a sample of 7- to 9-year-old Nicaraguan children. These findings make sense, given other results documenting the impacts of pesticide exposure on children’s attention processes. Clearly, more research on the impacts of pesticide exposure on the socioemotional functioning of young children is warranted.

Air pollution

With ongoing rapid industrialization and urban growth, poor air quality is a serious concern in much of the global South, as well as in newly industrial countries in general ( Bartlett, Hart, Satterthwaite, de la Barra, & Missair, 1999 ). Here we discuss work in both the global North and South documenting the impacts of exposure to air pollution, primarily resulting from proximity to industrial plants and to air and road traffic, on children’s cognitive and socioemotional development.

Among the most common pollutants to be studied for its effect on cognition is nitrogen dioxide (NO 2 ), a toxicant produced by fossil fuel combustion and thus closely linked to road traffic as well as gas stoves. In Quanzhou, China, exposure to traffic-related pollution was found to be associated with poor performance on neurobehavioral tests ( Wang et al., 2009 ). Similarly, Dutch children exposed to high levels of NO 2 at home were found to score lower on memory evaluations, while no similar correlation was found between NO 2 exposure at school and cognitive outcomes ( van Kempen et al., 2012 ). A related study of children living near London’s Heathrow airport, however, found no association between exposure to NO 2 and cognitive performance in nine- to ten-year-olds ( Clark et al., 2012 ).

In other work on air pollution, prenatal exposure to environmental tobacco smoke was negatively associated with cognitive performance at age two in African American and Dominican children in New York City ( Rauh et al., 2004 ). Within the same populations, exposure to high levels of airborne polycyclic aromatic hydrocarbons (PAH) (largely from road traffic fuel combustion) was associated with lower cognitive scores and moderate developmental delay at age three ( Perera et al., 2006 ), and lower IQ scores at age five ( Perera et al., 2009 ). Similarly, exposure to PAHs was significantly associated with lower non-verbal IQ scores among five-year-olds in Poland ( Edwards et al., 2010 ). In China, children living within proximity of a coal-fueled power plant were found to have higher cord PAH levels than those in both the New York City and Poland studies, and these levels were associated with a greater risk of delay in motor development and language abilities at age two ( Tang et al., 2008 ). There are also potentially prolonged consequences of overexposure to PAHs. Noting that children highly exposed to PAHs were 2.89 times as likely to have lower MDI scores than unexposed children at the age of three, Perera et al. (2006) suggested that greater exposure to such high levels of pollution could adversely affect language, reading and math abilities later on.

Changes in brain structure as a result of exposure to high levels of air pollution have been proposed as a possible explanation for resulting cognitive defects. In Mexico City, urban air pollution was found to be associated with prefrontal white matter hyperintense lesions in both children and dogs; these lesions are believed to be associated with poor cognitive outcomes ( Calderón-Garcidueñas et al., 2008 ). Calderón-Garcidueñas and colleagues found that 56.5% of children living in highly polluted Mexico City possessed such lesions, in comparison to just 7.6% of children living in Polotitlan, an area with lower levels of pollution. The former also performed more poorly on psychometric tests. However, seven- and eight-year-olds in Mexico City exposed to high pollution levels generally scored lower in evaluations of short-term memory, attention and learning ability than those in Polotitlan, whether they possessed such lesions or not ( Calderón-Garcidueñas et al., 2011 ). Thus, as Calderón-Garcidueñas and Torres-Jardon (2012) note, exposure to high levels of air pollution is just one aspect of the environmental inequalities experienced by children from lower socioeconomic backgrounds in both the global North and South ( Evans, 2004 ).

Against the backdrop of such settings as New York City, Mexico City, and the rapidly growing cities of China, the majority of the literature on the subject seems to suggest that the relation between air pollution and developmental outcomes is one largely tied to industrialization and urbanization. A notable exception is Munroe and Gauvain’s (2012) investigation of the association between indoor open-fire cooking—a common practice in the global South—and cognition in four communities: Garifuna in Belize, Logoli in Kenya, Newar in Nepal, and Samoans in American Samoa. A moderate negative correlation between indoor open-fire cooking and block building performance, memory, pattern recognition and structured play was found.

Water pollution, sanitation and access

Many families in the global South have limited access to clean water and sanitation facilities ( Bartlett, 1999 ; Bartlett et al., 1999 ; Walker et al., 2007 ). This section will outline the effects of water quality (specifically pollution and sanitation) on children’s cognitive and socioemotional development in the global North and South.

The most common water pollutant studied in relation to children’s development is arsenic. Rosado et al. (2007) found that amongst 6- to 8-year-old children attending school near a smelter complex in Torreón, Mexico, those with higher concentrations of urinary arsenic performed worse on several measures of cognitive and language development than did children with lower concentrations. This relationship was not impacted by lead exposure, demographics, or nutritional factors, although lower SES children had higher levels of urinary arsenic. Likewise Tsai et al. (2003) found that young Taiwanese adolescents exposed to arsenic in well water had lower scores than unexposed adolescents on cognitive assessments of memory and attention switching, even after controlling for education and gender. And in a study of 9.5- to 10.5-year-old children using tubewells in Bangladesh, Wasserman et al. (2004) found that water arsenic levels were associated with poorer cognitive functioning. Asadullah and Chaudhury (2011) similarly found that eighth grade children exposed to arsenic-contaminated tubewells in rural Bangladesh had lower mathematics scores than those not exposed, even when controlling for schooling history, prior arsenic exposure, and parental factors. Wang et al. (2007) likewise found that rural Chinese eight- to twelve-year-olds living close to wells with high levels of arsenic received lower IQ scores than those who did not, although it should be noted that this relationship was only documented for children with high levels of exposure, and sociodemographic factors were not controlled for.

High manganese levels in the public water system may also impact children’s behavior, as documented by Bouchard et al. (2007) in a study of 6- to 15-year-old children’s behavioral functioning in Canada. After controlling for potential confounding variables (age, sex and income), they found that hair manganese was significantly associated with hyperactivity and oppositional behavior, as measured by teachers’ report. Interestingly, the positive relationship between hair manganese and hyperactivity was greater for older children (above 11 years old).

Research suggests that a lack of proper water sanitation and waste management exposes many children to water-borne diseases. For example, Copeland et al. (2009) found that 30% of households in Brazilian shantytowns had fecal contaminated drinking water. Besides their health effects, water-borne diseases also have adverse developmental consequences for children. Guerrant and colleagues (1999) explored the relationship between diarrheal illness (a common water-borne disease) early in childhood and the cognitive functioning of 6.5- to 9-year-old children living in a Brazilian shantytown. A significant negative correlation was found between children’s cognitive functioning and early childhood diarrhea (see also Niehaus et al., 2002 for similar results). And Lima et al. (2004) found that the availability of garbage collection and access to a toilet partially explained differences in cognitive and psychomotor performance of low-income 12-month-olds living in northeast Brazil. Likewise, in an investigation of the environmental conditions (including poor access to drinking water, an inconsistent electricity supply and inadequate sewage drains) impacting 7- to 8-year-old children’s cognitive development in war-torn Baghdad City, Ghazi and colleagues (2012) found that below average water quality (as reported by parents) was associated with lower IQ scores, and that access to services (including water quality, electricity supply and access to grocery stores) independently predicted IQ, after adjusting for parent education and income.

In addition to direct impacts on cognitive functioning, diarrhea and intestinal parasites resulting from bacteria-contaminated water (often from sewage) contribute towards malnutrition and stunting, both of which impact children’s IQ and school performance, and may also contribute towards behavioral problems ( Bartlett, 2003 ). These associations may result as early malnutrition and exposure to environmental toxins and stress can alter both brain structure and function, thus leading to long-term changes in cognitive and socioemotional functioning ( Grantham-McGregor et al., 2007 ). In addition, both illness and malnutrition may lead to increased absences from school and attention problems when in school. Further, access to water may impact school attendance directly, particularly for girls in the global South, who frequently have to walk long distances to collect clean water ( Bartlett, 2003 ). Finally, it is worth noting that global climate change is likely to affect access to clean water for millions of low-income families in the global South, particularly in Africa and parts of Asia, in the next 20 years ( Bartlett, 2008 ).

A recent article suggests that contaminated drinking water in childhood may have lasting effects. Aschengrau et al. (2011) conducted a retrospective study of children from eight towns in the US who were exposed to water contaminated with tetrachloroethane (PCE, a solvent used in dry cleaning) during the prenatal period and/or early childhood. They found that, after controlling for parental SES and other potential covariates, highly exposed individuals had higher rates of cigarette, alcohol, and other drug use in adolescence and early adulthood.

Numerous studies in high-income countries reveal that chronic noise exposure early in childhood interferes with reading acquisition ( Evans, 2006 ). Although most studies are cross-sectional with statistical controls for SES, several studies have demonstrated a dose-response function. Adverse impacts on reading have also been replicated in prospective longitudinal studies with the introduction of a new major noise source such as an airport, as well as in experiments with noise attenuation interventions. Children in higher elementary school grades suffer greater adverse reading outcomes; this has been attributed to longer duration of exposure ( Evans & Hygge, 2007 ) but might also reflect greater awareness of noise (Dockrell & Shield, 2004). Some studies have also shown worse reading outcomes for children exposed to noise at home and school, bolstering the duration of exposure explanation. Children with poorer cognitive skills also appear more vulnerable to the induction of reading deficits from noise exposure ( Evans, 2006 ; Dockrell & Shield, 2006 ).

Several cognitive deficits reliably associated with noise exposure are candidate mechanisms for the well-documented noise – reading link. Long-term memory is adversely affected by noise, and attentional strategies are altered by noise exposure ( Evans, 2006 ). Interestingly, a few studies have also shown linkages between chronic noise exposure and deficits in auditory discrimination (e.g., phoneme perception), a critical aspect of speech perception ( Evans, 2006 ; Evans & Hygge, 2007 ). Speech perception is a major building block of reading acquisition. Finally, emerging work in neuroscience indicates potentially detrimental noise effects on brain speech function and structure ( Kujala & Brattico, 2009 ).

Chronic noise exposure, similar to many of the environmental conditions described herein, is not only aversive but also uncontrollable and sometimes unpredictable as well. Repeated exposures to uncontrollable as well as unpredictable events can undermine human motivation ( Cohen, Evans, Stokols & Krantz, 1986 ), thus impacting the persistence and effort needed (amongst other things) for academic achievement. The first human studies of learned helplessness employed uncontrollable noise as the induction stimulus ( Hiroto, 1974 ; Krantz, Glass & Schneider, 1974 ). Since then, many studies have shown that uncontrollable noise exposure can cause learned helplessness ( Evans & Stecker, 2004 ).

The bioecological perspective ( Bronfenbrenner & Morris, 1998 ) suggests a complementary set of processes that might also be related to noise and reading acquisition. Noise might alter caregiving behaviors salient to reading acquisition. We know, for example, that teachers in high noise impact schools alter their teaching methods and also complain about interruption and fatigue ( Evans, 2006 ). It is conceivable that parents might talk less to their children, be less responsive to children’s verbalizations, and not read aloud as much to their children in high noise settings.

Research on the relation between noise and children’s cognitive development outside of the United States and Europe is extremely limited. However, what evidence there is suggests that noise levels impact children in varying contexts similarly. Seabi, Goldschagg, and Cockcroft (2010) found that 9- to 13-year-old South African children attending a public school in a high aircraft noise area had poorer reading comprehension and reduced visual attention in comparison to a matched group of children attending a public school with typical levels of noise exposure. No differences in working memory were found, however. Clearly, further work in the global South is desperately needed, particularly as there is some evidence to suggest that noise levels might be significantly higher than in higher-income countries. For example, in a recent comparison of quiet versus noisy public schools in urban India, Lepore, Shejwal, Kim and Evans (2010) recorded a decibel level of 85 dBA. Since decibels is a logarithmic scale, and about 45 dBA is considered appropriate, this is very loud.

Outside of the global South, Hiramatsu and colleagues (2004) found deficits in long-term but not short-term memory among 8- to 11-year-olds residing proximate to a large air force base in Okinawa, Japan compared to their peers living in quiet areas. Similarly, Karsdorf and Klappach (1968) found that secondary school aged children attending noisy schools (proximate to road traffic) in Halle, former East Germany, had more focused attention problems compared to their peers in relatively quiet secondary schools. Finally, recent work in Belgrade, Serbia indicates that chronic residential noise exposure from road traffic can interfere with executive functioning, but only among elementary school aged boys ( Belojevic et al., 2012 ).

Evidence from both laboratory and field studies in North America and Western Europe shows that noise exposure is stressful, creating irritation and annoyance and elevating cardiovascular indicators of stress such as blood pressure and neuroendocrine stress hormones (e.g., cortisol) ( Evans, 2006 ; Paunovic et al., 2011). In most of these studies, resting physiological stress measures were taken under quiet conditions. Thus the indications of elevated stress are in relation to chronic noise exposure. There are more studies of aircraft relative to street traffic noise, with evidence for the former having stronger physiological impacts than the latter ( Evans, 2006 ). However, Babisch et al. (2009) found that a nationally representative sample of 8- to 14-year-old German children whose bedrooms faced a high traffic street had higher blood pressure than those with a bedroom facing a low traffic street. These relations were independent of various sociodemographic factors.

Studies in Slovakia ( Regecova & Kellcrova, 1995 ) and Serbia ( Belojevic et al., 2008 ; Paunovic et al., 2009 ) also revealed adverse impacts of road traffic noise on children’s blood pressure, even after statistically controlling for variables such as maternal education. Nine- to 13-year-old children residing near airports in Russia in the mid 1960s had higher blood pressure than their peers in quiet areas ( Karagodina, Soldatkin, Vinokur, & Klimukhin, 1969 ). In a study also conducted in the mid 1960s in former East Germany, Karsdorf and Klappach (1968) found that secondary school children attending urban schools located proximate to busier streets with higher noise levels had significantly higher resting blood pressure. Finally, Wu et al. (1993) found that, amongst 7- to 12-year-old Taiwanese children attending schools in high road traffic noise areas of Taipei, those with typical hearing had significantly higher blood pressure than those who were deaf.

Data are mixed on chronic noise exposure and children’s socioemotional development. Prospective, longitudinal data show that German elementary school children report lower levels of psychological well being with increases in noise exposure from aircraft ( Bullinger et al., 1999 ). A cross-sectional Austrian study of traffic noise reported a dose-response function between noise levels and teacher ratings of psychological well being among elementary school children if the child had biological risk factors such as prematurity or low birth weight ( Lercher et al., 2002 ). Two different cross-sectional studies of European school children have uncovered relations between aircraft noise exposure and elevated symptoms of hyperactivity ( Haines, Stansfeld, Brentnall et al., 2001 ; Stansfeld et al., 2009 ; but see Haines, Stansfeld, Job et al., 2001 ). None of these European studies found a link between noise levels and general, overall indices of psychological well being. Finally, Ristovska et al. (2004) compared several measures of mental health among 4 th grade children in Macedonian schools varying in traffic noise exposure. Children in the noisier schools had decreased social skills and more oppositional behaviors but were similar in levels of anxiety compared to their peers attending quieter schools. Recall also that, as indicated above, several studies have shown a link between chronic noise exposure and elevated learned helplessness among children ( Evans, 2006 ).

The most consistent crowding metric with human consequences is people per room. Indices of external density such as people per census tract typically yield no associations with human behavior ( Evans, 2006 ). Studies that have teased apart residential density from family size find the former rather than the latter to be the more critical variable. Although many believe there are differences in tolerance for crowding across different cultural contexts, the cognitive and behavioral development of children living in as diverse contexts as the United States, India, Thailand, Egypt, Hong Kong, South Africa, and Jamaica indicates similar developmental correlates of crowding in both residential and school settings ( Evans, 2006 ; Liddell & Kruger, 1987 ; 1989 ; Wachs & Corapci, 2003 ).

It is important to note that children in the global South, relative to North America and Western Europe, tend to live in more crowded home environments. For example, Evans, Lepore, Shejwal and Palsane (1998) found that densities (people per room) among primarily working class Indian families ranged from .67 to 5 persons/room, with a mean of 1.81. The US Census considers > 1 person/room to be crowded.

Significant research across multiple contexts documents the impacts of crowding on general school achievement and IQ, reading comprehension, and object spatial relations ( Evans, 2006 ). In a study of low-SES rural Eygptian 3- to 6-month-olds, Rahmanifar et al. (1993) found that infants in more crowded households were more lethargic and drowsy, conditions associated with delayed development. In their examination of 12-month-old children of recent Haitian immigrants to the US, Widmayer and colleagues (1990) found similarly that residential crowding was linked to delays in psychomotor, but not cognitive development. These associations may result from disruptions of children’s exploration, play and engagement with both objects and people in their immediate environments (Heft, 1985; Liddell & Kruger, 1987 ; 1989 ).

Crowding in educational environments has also been linked to more off-task time ( Kantrowitz & Evans, 2004 ; Krantz, 1974 ). For example, Liddell and Kruger (1987) found that levels of crowding within a crowded urban South African childcare center were negatively associated with 32- to 64-month-old children’s levels of cooperative play and positively associated with the percentage of time spent unoccupied. In a follow-up study, they found that children from more crowded homes spent less time engaged in play with objects, more time unoccupied, and more time as onlookers ( Liddell & Kruger, 1989 ). Similarly, in an investigation of relationships between the home environment and Egyptian toddlers’ adaptive behavior, Wachs et al. (1993) found that 24- to 29-month-olds’ simultaneous involvement with persons and objects in their environment was negatively correlated with density.

Residential crowding can also disrupt parent-child interactions ( Evans, 2006 ; Wachs & Corapci, 2003 ). In more crowded homes, parents talk less with their infants and toddlers ( Wachs et al., 1993 ) and use less complicated vocabulary and sentence structures with their toddlers ( Evans, Maxwell, & Hart, 1999 ). Not surprisingly, in an investigation of the influences of parental SES on South African children’s outcomes, Goduka et al. (1992) found that crowding predicted 5- to 6-year-old vocabulary scores. Children’s physical development and quantitative skills were also adversely associated with household crowding.

Evans et al. (1998) showed that some of the adverse effects of residential crowding, statistically controlling for SES, on Indian elementary school children’s academic achievement were mediated by heightened family conflict. Another variable that may help account for the link between household crowding and diminished academic achievement is inadequate space to do homework. In a study of low-income families living in apartments in Singapore, Hassan (1976) found an inverse relationship between apartment square footage and school performance among children. More crowded apartments also had inadequate privacy for students to study. The latter relation was also reported among secondary school pupils living in apartments in Hong Kong ( Mitchell, 1971 ). These effects of crowding on children’s cognitive functioning have similarly been reported in North America and Western Europe ( Evans, 2006 ), with consistent differences found for standardized achievement scores in grade school children. Moreover, the adverse associations uncovered between residential density and diminished academic achievement continue through secondary school, independent of family SES ( Evans, 2006 ). In addition, in an instrumental variable analysis of national data in France, Goux and Maurin (2005) showed that the probability of having to repeat a grade among 15-year-olds was strongly linked to overcrowding in the household.

Crowded home and school environments significantly impact the behavior and socioemotional functioning of both children and their parents ( Evans, 2006 ; Wachs & Corapci, 2003 ). For example, Ani and Grantham-McGregor (1998) found that crowding independently predicted Nigerian elementary school boys’ levels of aggressive behavior in school. Parental perceptions of residential crowding were inversely associated with positive social behavior amongst 3- to 35-month-old Burundian refugee children living in the United States ( McAteer, 2012 ). Interestingly, in a study of feeding practices in Jamaican primary schools, Grantham-McGregor, Chang, Walker, and Powell (1998) found that the negative impacts of classroom crowding on children’s behavior were exacerbated by poor nutrition.

One of the effects of high-density living may be greater difficulty monitoring and regulating children’s behaviors. Less parental monitoring is a well-documented predictor of behavioral conduct disorders, including juvenile delinquency. Parents in both Singapore (Hassan, 1976) and Hong Kong ( Mitchell, 1971 ) noted greater difficulties monitoring their children as a function of household crowding, and in the former case this appeared to contribute to greater juvenile delinquency rates.

Greater family conflict and tension have been reported among crowded Indian and Thai families ( Evans et al., 1998 ; Fuller et al., 1993 ), and a number of studies in low-income countries have documented positive associations between household crowding and physical punishment of children ( Afifi, El-Lawindi, Ahmed, & Basily, 2003 ; Youssef et al., 1998 ; Gage & Silvestre, 2010 ; Sumba & Bwibo, 1993 ; Vega-Lopez et al., 2008 ). In a survey of parenting values conducted in 34 low- and middle-income countries around the globe, Cappa and Kahn (2011) documented a relatively consistent link between household crowding and maternal endorsement of the need for physical punishment in child rearing.

In high-income countries both children and parents report more strained, negative familial interactions in high-density homes ( Evans, 2006 ), as well as instances of elevated punitive parenting practices. Children in more crowded preschools and elementary schools also evidence more aggressive behaviors towards their classmates ( Evans, 2006 ). One of the factors believed to drive part of the crowding – aggression link is conflict over scarce resources such as toys ( Evans, 2006 ).

One of the ways in which crowded family members appear to cope with crowding is to socially withdraw from one another, which can have the unintended consequence of diminishing socially supportive relationships ( Evans et al., 2001 ). A number of studies, including some with random assignment, have shown that crowded children tend to be more socially withdrawn ( Evans, 2006 ). Parents in more crowded homes are also typically less responsive to their children ( Evans, 2006 ).

Given greater social withdrawal among children in high-density homes and lower levels of parental responsiveness in similar situations, some investigators have explored whether crowding might also be linked to psychological distress among children. As indicated above, there is already evidence of elevated rates of aggression, withdrawal, and behavioral conduct disorders such as juvenile delinquency. A small number of studies in North America and Europe have shown that children in more crowded homes have higher levels of psychological distress ( Evans, 2006 ). They are also more susceptible to learned helplessness ( Evans, 2006 ; Evans & Stecker, 2004 ). This effect has been produced in a laboratory experiment on crowding and persistence on puzzles, and at least two field studies showed a dose-response function between residential density and learned helplessness ( Evans, 2006 ).

In a study of 10- to 12-year-old Indian children, Evans et al. (1998) showed that residential density was inversely related to teacher ratings of behavioral adjustment at school, and elevated conflict and lower levels of social support within the family. SES was included as a statistical control. For girls but not boys, density was also related to learned helplessness. Family conflict partially mediated these relationships. The authors also found that resting blood pressure was elevated among more crowded boys, but not girls. This matches several studies indicating elevated indices of physiological stress among children living in more crowded homes or attending more crowded schools/childcare ( Evans, 2006 ).

Household chaos

Research on children’s environments focuses on the intensity of exposures, largely ignoring temporal issues such as duration and stochasticity. The paucity of research on duration of exposure is unfortunate, particularly in thinking about the maturation of developing processes over time. This section brings attention to another largely unexamined property of children’s environments – their degree of structure and predictability. One of Urie Bronfenbrenner’s fundamental contributions to child development was the insight that proximal processes, the exchanges of energy between the developing child and the persons and objects in their immediate settings, need to occur on a regular, sustained basis in order to be effective ( Bronfenbrenner & Morris, 1998 ). Bronfenbrenner also argued that proximal processes need to be reciprocal between the child and her surroundings and become progressively more complex as she matures. Settings that are unpredictable and unstructured may destabilize children’s development because they interfere with effective proximal processes ( Bronfenbrenner & Evans, 2000 ; Bronfenbrenner & Morris, 1998 ). This thinking has led to emerging interest in chaos and children’s development ( Evans & Wachs, 2010 ; Fiese, 2006 ). Most studies use parental or observer ratings of levels of structures and routines coupled with indications of noise, crowding, and various other interruptions of household activities to evaluate levels of chaos. Evans and Wachs (2010) , in a recent volume on chaos and child development, provide an in-depth discussion of the measurement of chaos.

Chaos has been linked, primarily in cross-sectional studies in North America, to academic achievement and socioemotional development, including behavioral conduct difficulties and symptoms of internalization (e.g., depression, anxiety) ( Ackerman & Brown, 2010 ; Fiese & Winter, 2010 ). Chaos has also been linked to deficits in self-regulation and learned helplessness ( Brody, Flor, & Gibson, 1999 ; Evans, Marcynyszyn, Gentile, & Salpekar, 2005 ) and comprehension of social cues ( Dumas et al., 2005 ).

Although the majority of the work on chaos and child development has been conducted in Western contexts ( Wachs & Corapci, 2003 ; Weisner, 2010 ), a recent study by Shamama-tus-Sabah, Gilani, and Wachs (2011) found that levels of chaos in the homes of 8- to 11-year-old Pakistani children uniquely predicted internalizing and externalizing behavioral problems and lower levels of adaptive behavior, as rated by both mothers and teachers. No relations between chaos and cognitive development were found. Using the same data set, Shamama-tus-Sabah and Gilani (2010) also found that home chaos predicted children’s conduct problems. Clearly, further work in low-income countries is warranted, particularly as at least some components of chaotic environments (specifically the interruption of daily routines, and thus children’s proximal processes) likely impact children growing up in the global South in similar ways to their American and European counterparts ( Wachs & Corapci, 2003 ; Weisner, 2010 ).

Residential mobility

Poverty, substandard housing, and slum dwellings without security of legal tenure often lead to excessive residential mobility. Reliable housing is critical for children’s security and stability, and is essential if families are to establish daily routines ( Bartlett et al., 1999 ). High levels of residential mobility in North America are associated with poorer psychological adjustment, less socially supportive peer relationships, and deficits in academic achievement ( Adam, 2004 ; Jelleyman & Spencer, 2008 ; Oishi, 2010 ). In addition, students and teachers in classes with high levels of mobility face considerable challenges because of the instability of their members. Early childhood residential instability can also influence developmental trajectories. Adolescents with more frequent moves tend to have diminished social networks and hold comparatively less central positions therein ( South & Haynie, 2004 ), and are vulnerable to earlier onset of sexual activity ( South, Haynie, & Bose, 2005 ). Bures (2003) , using a nationally representative sample of middle-aged American adults found that more frequent moves during childhood were associated with poorer mental health and more strained social relationships in midlife, independent of race, income, and education.

In the global South, residential mobility is high, particularly for low-income families living in urban areas ( Bartlett et al., 1999 ), who frequently face forced evictions ( Chatterjee, 2007 ). Although little work in the global South has directly evaluated the impacts of high residential mobility on children’s cognitive and socioemotional functioning, it is likely that high mobility disrupts proximal processes ( Bronfenbrenner & Evans, 2000 ). Further, children whose families are evicted from their homes in a violent manner may experience trauma. For example, Dizon and Quijano (1997) have documented the impact of violent forced evictions in Manila on young children’s emotional functioning, noting that many children report recurring nightmares and/or become withdrawn.

An extensive body of international research, much of it employing adapted versions of the HOME scale ( Bradley & Caldwell, 1980 ), has documented the impacts of the quality of the home environment on children’s cognitive and socioemotional development ( Bradley & Corwyn, 2005 ; Evans, Wells, & Moch, 2003 ; Iltus, 2007 ). The HOME scale and its variants, however, primarily consist of indices of parent-child interactions, with fewer items focused on the physical environment. Furthermore, most studies with the HOME do not look at the impacts of individual physical environment items on children’s developmental outcomes. Wachs and colleagues’ Purdue Home Stimulation Inventory (PHSI, Wachs, Francis, & McQuiston, 1979 ) provides more detailed information about the quality of the physical environment experienced by children, but it has not been employed as widely. In addition, although the HOME has been widely used in various cultural contexts, the scale as a whole, and the physical environment items in particular, may not adequately assess the full range of physical affordances offered by housing for children, particularly in the global South ( Hayes, 1997 ; Iltus, 2007 ; Ngorosho, 2010). In this section, we focus on what is currently known regarding the effects of housing type, physical housing quality, and the availability of resources for children, such as books and toys in the home.

Housing type

Research on housing type in more affluent countries has focused primarily on the potential developmental implications of high-rise housing. There is a long history of popular discourse about the allegedly harmful effects of living on the upper floors of large buildings on children’s development. These concerns are rooted in the association of large, multistory housing blocks with crime in public housing in the US, and with well-documented associations between building scale and crime ( Newman, 1972 ; Taylor & Harrell, 1996 ). However, although a few studies in high-income countries have shown an association between children’s academic achievement and residence in high-rise compared to low-rise buildings, there are also several non-replications of these relations ( Evans, 2006 ; Evans et al., 2003 ). One study showed that the effects held only for boys, which could also explain the mixed set of findings since most studies have not investigated gender differences in response to high-rise housing ( Saegert, 1982 ).

Several studies in high-income countries have found that children and youth in high-rise buildings manifest greater levels of behavioral conduct disorders (e.g., delinquency, aggression) ( Evans, 2006 ; Evans et al., 2003 ). In an investigation of relationships between high-rise dwelling and Japanese children’s behavior, Oda, Taniguchi, Wen, and Higurashi (1989) found that infants living on lower floors received higher scores on independent behaviors (such as greeting and potty training) than did those living on higher floors. However, these differences were not significant for kindergartners. These findings largely mirror those in Western contexts ( Evans, 2006 ; Evans et al., 2003 ). In addition, although children’s outcomes were not measured, Levi, Ekblad, Changhui, and Yuequin (1991) found that parents living in high-rise apartments in Beijing showed anxiety regarding the lack of easily monitored play spaces for children. In a study of families living in high- versus low-rise apartments in Israel, Churchman and Ginsberg (1984) similarly found that the outdoor play behavior of 4- to 5-year-old children living in high-rises was more restricted than that of other children, although it should be noted that these effects were not found at other ages (within the range of 2–13 years).

In the global South, housing type is inextricably connected to housing quality. There is little research investigating the impacts of housing type alone. Further, the variations in housing type are somewhat different from those in the global North, with high-rise dwellings being uncommon. However, there is some evidence that a high percentage of families, particularly low-income families in urban areas, live in informal housing, and that such housing often lacks basic amenities such as access to clean water ( Bartlett et al., 1999 ; Hall & Lobina, 2006 ). The implications of an unclean water supply have already been discussed above. In addition, informal housing is typically unstable, and children living in such areas frequently face eviction and therefore frequent residential mobility ( Bartlett et al., 1999 ), the implications of which have already been discussed. In addition, children living in informal housing may be more vulnerable to injury, and are more likely to be exposed to toxins from industrial waste. And children who are homeless or who live in informal housing may be less likely to attend school, as they lack a formal address ( Wegelin & Borgman, 1995 ). For example, a recent survey in Delhi found that only 54.5% of children in slums enrolled in school, as compared to 90% across the city as a whole ( Aggarwal & Chugh, 2003 ). For those in school, homelessness has significant impacts on school performance and socioemotional well being ( Hicks-Coolick et al., 2003 ; Neil & Fopp, 1992 ). There is also some evidence that children’s self-esteem is negatively impacted by residence in slum dwellings and other informal settlements ( Kruger, 2002 ).

In addition to direct effects, housing type may interact with other physical characteristics of children’s early environments to influence human development. Delays in cognitive development associated with residential density among preschool children are attenuated if children have access to a room where they can spend time alone ( Wachs & Gruen, 1982 ). Negative self- and teacher-ratings of Austrian primary school children’s psychological well being in more crowded homes are exacerbated by residence in multi-family complexes in comparison to living in either single family or small row family housing units ( Evans, Lercher & Kofler, 2002 ).

Housing quality

With ongoing urbanization, the number of families living in substandard housing in the global South is only likely to increase ( Chawla, 2002 ; Meng & Hall, 2006 ). In addition, there is some evidence that indigenous populations in Australia, for example, are disproportionately exposed to substandard housing ( Dockery et al., 2010 ). Yet most research to date on housing quality and children’s development has been conducted in the US and Europe ( Bradley & Putnick, 2012 ; Evans, 2006 ; Leventhal & Newman, 2010 ). There is a desperate need for further work in this area.

A small number of studies in North America and Europe have examined housing quality and cognitive development. A few, including a large national British cohort, reveal that, independent of SES, children living in substandard housing have lower academic competency ( Evans, 2006 , Evans et al., 2003 ). These effects are amplified by duration of exposure to substandard housing ( Douglas, 1964 ), and one study showed that when families moved into better housing, elementary school performance improved ( Wilner, Walkley, Pinkerton, & Tayback, 1962 ). Dunifon, Duncan and Brooks-Gunn (2004) , using a US national data set, also showed that residential clutter during childhood predicted adult educational attainment.

A number of cross-sectional studies in North America and Europe show that children living in substandard housing suffer from greater psychological distress ( Evans, 2006 ; Evans et al., 2003 ). Nearly all of these studies incorporate statistical controls for SES, and the effects replicate in longitudinal studies examining changes in housing quality (cf., Blackman & Harvey, 2001 ). Learned helplessness is also greater among children living in substandard housing, with statistical controls for SES ( Evans, Saltzman & Cooperman, 2001 ), and two studies reveal elevated physiological stress among low-income children inhabiting poorer quality housing. In a cross-sectional study, low-income primary school children living in substandard housing coupled with noise and crowding had higher levels of overnight stress hormones (e.g., cortisol) ( Evans & Marcynyszyn, 2004 ). In a second, longitudinal study, low-SES children residing in lower quality housing had elevated cortisol over their first four years of life (Blair et al., 2011). Differences were already present at 7 months of age.

An important conceptual limitation of North American and European research is the rather limited range of variation in housing quality. Because of building codes and general levels of affluence, “bad” housing in these contexts is a lot better than most of the housing found in the global South. Note that, unlike the potential problem of unaccounted for confoundings in cross-sectional research that might lead us to over-estimate the impacts of housing quality on children’s development, the truncated range in housing quality leads to the opposite estimation bias.

A high percentage of children growing up in the global South live in substandard housing ( Bradley & Putnick, 2012 ; Govender et al., 2010) constructed with inferior building materials, leaking pipes, and cracks or holes in the walls and ceilings ( Chaudhuri, 2004 ). In 2002 it was estimated that more than half the housing units in Zimbabwe, 52.6%, were considered semi-permanent dwellings ( United Nations Statistics Division, 2012 ). In 2010, 30.6% of the housing units in Mexico did not posses basic amenities such as bathrooms, kitchens, and piped water within the household.

Substandard living conditions lead to higher levels of exposure to lead and other toxins, air pollutants and pests ( Govender et al., 2011 ). In addition, poor quality housing, and particularly unsafe dwellings, place additional stress on low-income parents already facing multiple stressors (Evans & English, 2002). This may result in parental fatigue and thus reduce caregivers’ capacity to be warm and responsive ( Bartlett et al., 1999 ; Bradley & Putnick, 2012 ; Evans et al., 2003 ; Leventhal & Newman, 2010 ). Furthermore, in unsafe home environments parents and other caregivers may constrain children’s play and other activities, so as to reduce the risk of injury ( Bartlett et al., 1999 ; Bradley & Putnick, 2012 ; Evans et al., 2003 ; Ferguson, 2002). Such constraints are not unfounded: Dal Santo et al. (2004) found that preschoolers’ estimated risk of unintentional injury is almost four times greater for a child living in a household needing repair. In rural sub-Saharan African contexts, limited space renders household items like kerosene easily accessible for children, and open fires for heating and cooking pose a serious injury risk ( Munro et al., 2006 ). Play constraints in particular likely have important implications for children’s cognitive and socioemotional development, given the importance of play for healthy development ( Bartlett, 1999 ; Milteer et al., 2012 ).

Research on direct impacts of housing quality on children’s cognitive and socioemotional development in the global South is very limited. However, in one study Ferguson (2008) found that the quality of Malawian orphanages appears to be associated with infants’ cognitive functioning. Space and furnishings (e.g., room arrangement, displays for children) predicted children’s cognitive outcomes. This effect may partially be explained by the fact that the provision of separate, soft, cozy areas for children may both offer comfort and help regulate social interaction. Such processes may help counter some of the negative effects of crowding and institutionalization on children. In addition, separate, enclosed areas with comfortable furnishings provide a more homey, and less institutional, setting for young children ( Evans, 2006 ; Greenman, 1988 ; Olds, 2001 ; Sanoff, 1995 ).

Given the limited work directly linking housing quality to children’s developmental outcomes in the global South, further research in this area is desperately needed. One useful data source may be the Multiple Indicator Cluster Survey (MICS), an international household survey that has been implemented across a large number of countries in the global South.

Resources for children

Another aspect of the physical environment that may influence young children’s development is the availability of learning materials ( Bradley & Corwyn, 2005 ; Bradley & Putnick, 2012 ). However, the availability of such materials is seldom disentangled from parent-child interactions in the literature. Nevertheless, there is a strong relation between income and the provision of both stimulating materials and experiences for young children from birth through adolescence (e.g., Bradley, Burchinal & Casey, 2001 ; Evans, 2004 ; McLoyd, 1998 ). And, several studies have shown that cognitive enrichment in the home mediates much of the co-variation between parental income and child cognitive development ( Duncan, Brooks-Gunn, & Klebanov, 1994 ; Linver, Brooks-Gunn, & Koben, 2002 ; Smith, Brooks-Gunn, & Klebanov, 1997 ). Access to other material resources such as electricity, a radio, a television, a telephone and transportation may also impact children’s cognitive development in particular ( Bradley & Putnick, 2012 ).

There is much debate over what constitutes appropriate learning materials in the home, particularly cross- culturally ( Bornstein et al., 2012 ; Bradley & Putnick, 2012 ; Ferguson, 2008 ). Nevertheless, the UNICEF-developed MICS, which has been adopted for use in evaluating factors contributing towards the well being of women and children by a large number of governments worldwide, includes items evaluating the number of books, the number of children’s books, and the availability of various types of homemade and store-bought toys and other play materials. There is some evidence that such materials are rarely available in the global South and in rural areas of newly industrial countries such as India, Thailand and China ( Bradley & Putnick, 2012 ). The availability of other material resources in the home is likewise limited.

A retrospective evaluation of developmental impacts of the availability of learning materials and material resources associated with modernity (writing tablets, books, electricity, piped water, a radio, a television, and a transportation vehicle) on children’s cognitive development at ages 3, 5, 7 and 9 years in Belize, Kenya, Nepal and American Samoa was conducted by Gauvain and Munroe (2009) . Access to these resources was positively correlated with children’s general cognitive functioning, perspective taking, and levels of exploratory play. Similarly, Hamadani et al. (2010) found that, after controlling for socioeconomic variables, the variety of play materials and the availability of magazines and newspapers in rural Bangladeshi homes independently predicted 18-month-olds’ cognitive development. And, in Ferguson’s (2008) investigation of relations between the quality of the physical environments of Malawian institutions and infants’ developmental functioning, access to learning materials independently predicted infants’ language and socioemotional development.

Schools and childcare

Unfortunately, continual innovation in the design of schools and classrooms throughout the world is typically not based on evidence, instead reflecting current trends in architecture and design ( Lackney, 2005 ). Much of instructional facility innovation at present is driven by the infusion of information technology into learning environments. Although this practice has some potential benefits, we simply do not know how to train teachers and designers in the use and configuration of learning environments to take advantage of the affordances offered by information technology in schools. This explosion of learning technologies in the West inevitably will be transported to the global South. Yet evidence to date from low-income countries indicates no clear impacts of exposure to computers and other related technologies on children’s academic achievement ( Glewwe, Hanushek, Humpage, & Ravina, 2011 ; Riddell, 2008 ).

There is a significant body of research investigating the impacts of school quality on children’s school achievement ( Evans, 2006 ; Glewwe et al., 2011 ; Irwin, Siddiqi & Hertzman, 2007 ; Riddell, 2008 ). However, as is true for the work on home environments, little research has specifically investigated the impacts of the physical environment of schools on children’s developmental outcomes, particularly in the global South. Most research in the US and Europe on the physical characteristics of educational settings has focused on open versus traditional plan configurations ( Evans, 2006 ). Because this issue has tangential relevance at best to children throughout most of the world, we focus here instead on school and classroom size; the quality of building infrastructure (structural quality, lighting, and indoor climate, and access to electricity, water and sanitation); and access to basic resources (classroom furniture, blackboards, books, computers, laboratories and libraries), as these have the clearest documented impact on children’s school achievement in the global South ( Glewwe et al., 2011 ; Riddell, 2008 ).

School and classroom size

There is a large body of research on school and classroom size. Because nearly all of this work has been conducted within the US and Western Europe, we do not know what happens when much larger scale schools or bigger classrooms occur. Although there is some variation across regions, primary school pupil-teacher ratios (PTRs) in the global South are typically much higher than those in the global North. For example, compare PTRs of 81:1 (Central African Republic), 76:1 (Malawi), 61;1 (Chad) and 58:1 (Rwanda) to 18:1 (UK), 14:1 (US) and 13:1 (Germany) ( World Bank, 2012 ). Notably, though, PTRs in East Asia and the Pacific (average: 17.9:1) and Latin America (22:1) are much lower than in South Asia (40:1) and sub-Saharan Africa (42.5:1).

Students in smaller schools in the US and Western Europe perform slightly better on standardized tests and feel more connected to their school ( Evans, 2006 ). There is some evidence that the benefits of smaller school size are greater for low-income children, and for children in lower grades ( Woessmann & West, 2006 ). Similarly, classroom size research yields a relatively consistent picture of small, adverse effects on children in both high- and low-income countries with increasing size ( Blatchford, 2003 ; Ehrenberg, Brewer, Gamoran, & Willms, 2001 ; Woessmann & West, 2006 ). For example, in an investigation of linkages between school physical quality and rural Kenyan first grade children’s cognitive functioning and behavior, Daley et al. (2005) found that the number of students per classroom predicted levels of off-task behavior and teachers’ ratings of general behavioral functioning. There is also some evidence that smaller classrooms support more student- as opposed to teacher-directed learning and, similar to school size, are associated with more socially supportive settings ( Blatchford, 2003 ; NICHD Early Child Care Research Network, 2004 ).

It is worth noting that both school and classroom size are confounded with crowding. Work on household size and density shows that the critical variable is density, not family size ( Evans, 2006 ). Insufficient work exists to tease apart school/class size from crowding.

Physical quality

A surprisingly large number of school spaces for American children are in disrepair. In a 2000 survey of school principals in 32 countries in both the global North and South, nearly 30% of US principals noted that the quality of their school’s buildings and grounds impacted student learning, and almost 40% noted the same for available instructional space ( Ahlehfeld, 2007 ). Estimates were much higher for the majority of other participating countries, including the United Kingdom, Norway, Turkey, Uruguay and the Slovak Republic. In the global South, the majority of rural schools in particular have inadequate building facilities, including a lack of finished flooring ( Glewwe et al., 2011 ; Riddell, 2008 ). In many countries, half to two thirds of schools lack electricity, water, and basic sanitation facilities ( UNICEF, 2010 ). For example, the 2005 UNESCO EFA Global Monitoring Report found that just 39% of classrooms in Senegal had sanitation facilities, and even fewer (33%) had access to drinking water.

One important limitation in most work on educational settings and student achievement, however, is over-reliance on school professionals’ ratings of building quality. Since teachers and administrators are well aware of children’s achievement profiles in their own schools and are themselves likely affected by building quality, the potential for spurious associations in this measurement approach is considerable. However, assessments of building quality conducted by independent raters (e.g., structural engineers) have also been consistently associated with standardized test scores ( Evans, 2006 ). Further strengthening these conclusions are several studies comparing performance before and after building improvements ( Evans, 2006 ). In two recent studies utilizing the New York City school facilities building quality database, Duran-Narucki (2008) showed that the significant association between these expert rating measures of school building quality and academic achievement in elementary school children was largely mediated by attendance. Moreover children in New York City primary schools with higher rates of student mobility suffer even worse achievement outcomes as a function of substandard school facilities ( Evans, Yoo, & Sipple, 2010 ).

Given that nearly all of the research on school facility quality and student performance emanates from wealthy countries where the range of school quality is truncated, this is an area of particular importance to examine in the global South where the range of quality is considerably broader. And, in fact, improvements in the physical structure of schools in the global South do appear to positively impact students’ test scores ( Glewwe et al., 2011 ). However, the research to date in this area is very tentative, and typically the schools being compared have multiple factors that differ in quality, making it difficult to clearly identify individual influences on children’s outcomes.

In a recent meta-analysis of the research to date on the impact of school quality, including both physical and psychosocial factors, on children’s school achievement in low-income countries, Glewwe et al. (2011) found that there appears to be good evidence for the impact of access to electricity on children’s educational outcomes. And, in their investigation of the relations between school physical quality and rural Kenyan first grade children’s cognitive functioning and behavior, Daley et al. (2005) found that the availability of natural light (in schools without electricity) predicted students’ test scores. In high-income countries, where lighting is typically sufficient, research has focused more on potential benefits of exposure to natural light. Although the work on natural light exposure and children’s health and performance is limited, some rigorous work suggesting the potential importance of natural light for young children has been conducted in Sweden ( Küller & Lindsten, 1992 ). These investigators found evidence for the importance of sufficient natural light exposure for primary school children’s well being during periods of the year when daylight hours are limited.

In North America, upper respiratory infections, asthma and allergies are the most common cause of primary school absenteeism and have been routinely linked to exposure to mold and other allergens as well as ambient pollutants inside both schools and children’s homes ( EPA, 2003 ). Poorly maintained heating and ventilation systems as well as low levels of indoor:outdoor air exchange exacerbate these adverse indoor climate impacts on children ( Evans, 2006 ). Although work in this area in the global South is limited, similar impacts of poor quality ventilation and heating would be expected.

Consistent with the bioecological perspective ( Bronfenbrenner & Morris, 1998 ), in addition to focusing on the direct effects of school setting physical conditions on children themselves, it is important to keep in mind that substandard working conditions influence labor satisfaction and retention, and the same holds true for teachers. Several studies have shown that poor quality school physical conditions adversely influence teacher satisfaction and retention ( Buckley et al., 2004 ).

In the global South, there is some evidence that access to basic resources in school environments, such as a sufficient number of desks, tables and chairs; access to blackboards; access to textbooks and other books; and the availability of a school library all impact children’s school achievement ( Glewwe et al., 2011 ; Riddell, 2008 ). However, frequently these physical environment factors are correlated with each other and with other physical and psychosocial factors such as class size, building quality and teacher training, and so it can be difficult to clearly identify key factors impacting child outcomes. In addition, the mechanism explaining learning outcomes is somewhat unclear; perhaps the availability of these resources partly signals a commitment on the part of the school administration and relevant local and national government agencies to quality education ( Glewwe et al., 2011 ). Nevertheless, a number of carefully controlled studies across multiple contexts document the importance of having a desk, chair and textbook per student. For example, in their investigation of the relations between school physical quality and rural Kenyan first grade children’s cognitive functioning and behavior, Daley et al. (2005) found that the number of books per student independently predicted standardized test scores.

In preschool and childcare settings across the global South, there is a growing interest in improving the quality of both physical and psychosocial environments for children ( Engle et al., 2007 ; Hyde & Kabiru, 2003 ; Irwin et al., 2007 ; Myers, 1992 ; van der Gaag & Tan, 1998 ). And, indeed, the most commonly used assessment of the quality of childcare environments, the Early Childhood Environment Rating Scale (ECERS, Harms, Clifford & Cryer, 1998 ), includes two rating scales that assess children’s interactions with the physical environment: Space and Furnishings and Activities (which includes both the availability of learning materials and their use). However, although a significant body of research in the United States indicates an association between childcare quality and children’s cognitive and socioemotional outcomes (e.g., Sylva et al., 2006 ), there is little research that considers the impact of the physical environment directly.

There is almost no work documenting the impact of the quality of childcare environments on children’s developmental outcomes in the global South. However, as part of a preschool intervention program in rural Bangladesh, Moore, Akhter and Aboud (2008) implemented a series of changes, including increasing the availability of learning materials for reading and mathematical problem-solving. They found that preschool scores on the Activities subsection of the ECERS-R increased, and that children’s cognitive outcomes and school readiness improved. However, it should be noted that the Activities subscale does not separate the availability of learning materials from their use. In addition, many researchers in the global South debate the applicability of the ECERS-R in evaluating childcare and preschool quality in non-Western contexts ( Aboud, 2006 ; Moore et al., 2008 ).

Neighborhood quality

Sadly, most children growing up in the global South live in neighborhoods of poor physical quality ( Bartlett, 1999 ; Chawla, 2002 ; Hardoy, Mitlin, & Satterthwaite, 2001 ). Physical characteristics of these environments include high levels of air and water pollutants; nonexistent or inadequate collection of household waste; poor drainage; poor sanitation; proximity to busy street traffic; and limited or absent access to childhood resources such as open green space, grocery stores, schools and hospitals and play space (e.g., Bartlett, 1999 ; Bartlett et al., 1999 ; Chawla, 2002 ; Hardoy et al., 2001 ; Kruger & Chawla, 2002 ). Many of these neighborhoods are also unsafe because of high traffic volumes and limited street lighting (e.g., Bartlett et al., 1999 ; Kruger, 2002 ; Kruger & Chawla, 2002 ). However, the research linking children’s cognitive and socioemotional development to neighborhood physical conditions, beyond those already discussed (exposure to toxins, air and water pollution, sanitation, and high mobility) is very limited. The situation is similar in high-income countries. There is a large literature on neighborhood quality and human health and well being ( Diez-Roux & Mair, 2010 ) and more specifically child development ( Leventhal & Brooks-Gunn, 2000 ), but this work is bereft of considerations of the physical environment of neighborhoods. In nearly all of the extant research, neighborhood quality is defined by the socioeconomic profile of the population. Two areas of neighborhood physical environment that are receiving considerable attention because of the obesity epidemic are access to places for physical activity and proximity to healthy food sources. This work, although still in its early stages, indicates that both of these neighborhood characteristics are related to obesity in children and are much more likely to be wanting in low-SES neighborhoods ( Diez-Roux & Mair, 2010 ; Evans, Wells, & Schamberg, 2010 ).

UNESCO’s Growing Up in Cities ( Chawla, 2002 ) provides some interesting insights into children’s experiences in neighborhood environments in Argentina ( Cosco & Moore, 2002 ), India ( Bannerjee & Driscoll, 2002 ) and South Africa ( Kruger, 2002 ). In all three contexts, children aged 10–15 years reported a keen awareness of the physical quality of their neighborhood environments, noting specific aspects of these environments (e.g., high traffic, litter, poor sanitation, a lack of open green spaces) that limited play opportunities. Similar data have been found among Australian primary school children ( Homel & Burns, 1989 ). Perhaps most salient in children’s narratives across these and the other contexts studied (Australia, the United Kingdom, the United States, Norway, Poland, South Africa) was the importance of access to green play spaces. Other work in low-income countries has similarly documented the importance of play spaces and access to natural settings for children (e.g., Bartlett et al., 1999 ). However, little work has specifically investigated the impacts of natural settings on the cognitive and socioemotional development of children in the global South.

Neighborhood physical quality

Parents rated their 9- to 12-year-old children in two Canadian cities as higher in psychological distress if the neighborhood was rated by trained observers as lower in physical quality ( Gifford & Lacombe, 2006 ). Both longitudinal and cross-sectional studies ( Diez Roux & Mair, 2010 ) show that neighborhood upkeep influences adults’ psychological distress. To illustrate the potential power of neighborhood physical quality on adult mental health, adjusting for income, race and neighborhood poverty, New York City adults living in poor quality neighborhoods were more than 30% more likely to suffer from depression in the past six months compared to adults residing in better physical quality neighborhoods (Galea et al., 2006). Psychological distress in adults is a central risk factor for healthy parenting.

Close proximity to street traffic caused Zurich parents to restrict children’s outdoor play activities, which in turn was associated with diminished social and motor skills among preschoolers ( Hüttenmoser, 1995 ). High levels of street traffic have also been associated with less social interaction among neighbors in San Francisco neighborhoods ( Appleyard & Lintell, 1972 ).

Natural settings

As has been discussed above, the majority of research on the impacts of access to the natural environment on children’s well being has taken place in the US and Europe. Parallel to findings in North America and Western Europe ( Evans, 2006 ), children across the global South prefer natural areas and engage in more complex levels of play in such settings ( Bannerjee & Driscoll, 2002 ; Bartlett, 1999 ; Bartlett et al., 1999 ; Chawla, 2002 ; Cosco & Moore, 2002 ; Kruger, 2002 ; Kruger & Chawla, 2002 ). Given the potential for access to natural play spaces to mitigate some of the impacts of poor quality physical environments on low-income children’s cognitive and socioemotional development, further work in this area is warranted. A few North American studies suggest that children’s executive functioning may be enhanced by access to nearby natural outdoor play spaces ( Evans, 2006 ), and a meta-analysis revealed that the greening of school yards across multiple sites in North America and Western Europe has been associated with improved academic performance and better psychological well being among pupils ( Bell & Dyment, 2008 ).

Evaluations of outdoor nature experiences such as Outward Bound in high-income countries reveal consistent, positive associations with psychological well being ( Hattie et al., 1997 ). Part of the apparent psychological benefits of access to outdoor play areas is likely related to enhanced physical activity, which has been consistently linked in both children and adults to proximate, outdoor recreational spaces ( Evans et al., 2010 ). In a recent WHO study of approximately 1200 6- to 18-year-olds residing in eight European cities, the well-documented, inverse relation between household income and childhood obesity was explained, in part, by proximity to open green space. Children from wealthier households had greater access to open green spaces, which in turn was linked to higher levels of physical activity. The latter largely accounted for the inverse, household income – body mass index correlation ( Evans et al., 2012 ).

Adults living in Los Angeles neighborhoods with more parks, independent of SES characteristics, perceived greater collective efficacy, an index reflecting greater social cohesion and social control ( Cohen, Inagami, & Finch, 2008 ). There are also several studies showing that adults’ physiological stress responses to aversive stimuli are attenuated by natural surroundings ( Evans, 2003 ). Thus some of the benefits of nearby nature for children may also operate via their parents. One study also revealed that children’s psychological reactions to stressful life events were attenuated by proximity to outdoor nature ( Wells & Evans, 2003 ).

Conclusions and future directions

As can be seen upon reviewing the current state of the evidence on the physical environment and child development, very little work has documented the impacts of environmental conditions on the development of children growing up in the global South and other low-income countries. This is unfortunate for many reasons. Foremost, the majority of the world’s children grow up outside of the affluent countries where most of the work has transpired. In fact, Bornstein and colleagues (2012) argue that less than 10% of developmental science research has studied communities that account for 90% of the world’s population.

What we do know suggests that the physical environment experienced by children impacts their cognitive and socioemotional development across the lifespan, from the prenatal period through adulthood. The development of interventions to improve the physical environments experienced by children across the globe is thus warranted. Interventions would also offer tremendous research opportunities to examine how environmental improvements can change developmental trajectories. This would also help address perhaps the major methodological weakness in most work on children and the physical environment: potential selection bias. Comparisons between children living in different environmental conditions nearly always face the alternative explanation that some individual characteristic rather than environmental conditions might be the root cause of developmental changes. Another critical reason for studying children in the global South and elsewhere outside of high-income countries is the severely restricted range of environmental conditions typically monitored in research on child settings in North America and Western Europe. Essentially every single environmental factor reviewed herein exists in a substantially greater range in low-income countries. Thus not only is 90% of the research on children and the environment from samples of less than 10% of children, the same goes for the environmental side of the equation. We know a reasonable amount about how variability within the top 10 or 20% of conditions matters. We know almost nothing about how variability from the top to the bottom 10% of environmental conditions affects children.

With these caveats in mind, the evidence to date documents adverse impacts of individual environmental risk factors, particularly environmental toxins and pollutants, on children’s cognitive development. However, the impacts on socioemotional functioning are less certain. In addition, the documented evidence for impacts of noise, crowding and chaos on the cognitive and socioemotional development of children growing up in the global South is tentative at best. And, across the globe, the impacts of individual aspects of the physical environment of housing, schools and neighborhoods are unclear, primarily because multiple factors tend to be correlated. This is especially true for low-income families, underfunded schools and poor neighborhoods in both the global North and South, where poverty is frequently associated with multiple environmental risks ( Evans, 2004 ; Ferguson et al., 2009 ). It is also important to recognize that when cumulative, environmental insults have been studied, they typically reveal worse outcomes than singular environmental risks ( De Fur et al., 2007 ; Evans, Li & Whipple, in press ). Furthermore, for low-income children, the confluence of deteriorating physical conditions along with inadequate psychosocial conditions is a primary, underlying pathway that helps account for the ill effects of poverty on child development ( Evans & Kim, 2013 ).

In order to better understand the effects of multiple environmental risk factors on children’s cognitive and socioemotional development, a holistic, multidisciplinary and multilevel approach that encompasses the complex interactions between biological, physical, and psychosocial factors impacting children’s developmental outcomes is needed. Such an understanding will allow us to more effectively intervene in children’s actual lived environments. In other work ( Ferguson & Lee, 2013 ), we have proposed a bioecocultural framework that integrates key components of Bronfenbrenner’s bioecological model ( Bronfenbrenner & Evans, 2000 ; Bronfenbrenner & Morris, 1998 ) with Nsamenang and colleagues’ ecocultural approach (e.g., Nsamenang, 1992 ; Nsamenang & Dawes, 1998 ), and Li’s (2003) cross-level dynamic biocultural coconstructivist paradigm (see also Boivin & Giordani, 2009 ). We thus focus here on outlining key steps involved in utilizing this framework to better understand and address the impacts of the physical environment on the cognitive and socioemotional development of children living in multiple contexts.

Developing and implementing a bioecocultural framework

The first step in developing and implementing a bioecocultural framework is to identify what is known and what is not yet known about the impacts of individual and intersecting environmental factors on children’s development. The present review, in conjunction with Evans’ (2006) earlier review that focused on Western contexts, does just that. We summarize the evidence to date below, while at the same time considering when the methodologies employed in related work are appropriate for filling in the gaps in the research literature, and when they are not. When they are not, it is important to identify what is currently known in a particular context, for example identifying relevant country-level statistics and databases. In addition, new tools for assessing children’s development in varying cultural contexts might be needed ( Ferguson & Lee, 2013 ; Nsamenang, 1992 ). Second, key factors, what public health researchers call “leverage points”, influencing children’s developmental outcomes should be identified (see Ferguson et al., 2009 ). Where possible, those leverage points most susceptible to change should be noted. Third, all of this information can be incorporated into an overarching bioecocultural framework, as outlined above, that identifies all known and hypothesized factors influencing a particular developmental outcome (e.g., literacy), key leverage points, known interacting influences between factors and, when possible, the mechanisms behind the relations between each factor and children’s development. Once this is done, interdisciplinary, international research teams should develop and implement a collaborative research program to test the model, with a specific focus on filling in the gaps in the research literature in understudied contexts, namely the global South. In doing this work, the intimate involvement of individuals, communities, local and national governmental agencies and researchers living in each context studied is essential ( Dawes & Donald, 2000 ; Weisner, 2010 ). In fact, ideally relevant individuals and communities should be involved in every stage outlined above. This will ensure that similarities and differences between contexts are adequately considered. Finally, in collaboration with all of these important constituents, key leverage points can be confirmed and leveraged in implementing a holistic program of reform that will effectively address current environmental inequalities, so as to ensure healthy developmental outcomes for all children.

Phase 1: Identifying influencing factors

Conceptually, given their direct impact on children’s biological systems, it is likely that environmental toxins and pollutants (specifically lead, mercury, PCBs, various pesticides, NO 2 , polycyclic aromatic hydrocarbons, environmental tobacco smoke, arsenic, manganese, and tetrachloroethane) impact the cognitive and socioemotional development of children living in different contexts similarly. The limited evidence to date indicates that this is the case. Further work on factors impacting socioemotional development is warranted, however, especially in the global South. Similarly, despite differences in adults’ perceptions of crowding and chaos, the evidence we have reviewed here suggests that factors contributing to chaos, including noise and crowding, likely impact children and adults across the globe in similar ways. However, given the limited work in this area, particularly in considering socioemotional development, these predictions need to be tested more thoroughly in low-income countries.

In terms of home and school environments, adequate building quality seems essential, but determining what this should entail in differing contexts is challenging. Home, classroom and school designs that reduce chaos may be particularly important. In addition, adequate lighting and comfortable climatic conditions (temperature, indoor air quality) are important for effective learning in school environments. Finally, the availability of key material and learning resources in both home and school environments appears to be particularly important for cognitive development, but the specific resources needed in differing contexts is unclear. Further work in this area is needed. Likewise, although it is clear that children growing up in low-income neighborhoods in both the global North and South encounter numerous disadvantages that impact their cognitive and socioemotional development, little is currently known regarding the specific components of the physical environment of neighborhoods impacting these developmental outcomes. Neighborhood physical quality is the most understudied aspect of the environmental characteristics discussed herein.

An important caveat at this point is that the majority of the work discussed in this review, with a few notable exceptions (discussed throughout), employs environmental and outcome measures developed in the West. Yet the specific components of the physical environment impacting child development in the global South may differ from those in the global North, as we have noted throughout. In addition, different cultural contexts, values and beliefs in the global South may mean that although, for example, there are documented impacts of lead on Egyptian children’s IQ scores, this aspect of children’s development may be less important than socioemotional competency in this context. Thus a consideration of what engenders competence within particular cultural contexts is essential ( Ferguson & Lee, 2013 ; Weisner, 2010 ).

The development of culturally appropriate assessments of both environmental quality and children’s developmental outcomes in the global South is sorely needed. This could, and should, go hand-in-hand with an evaluation of the effectiveness of a larger bioecocultural model in capturing the multiple environmental factors impacting children’s specific developmental outcomes in particular contexts, so as to provide a good test for the effectiveness of these methodologies in each context ( Ferguson, 2008 ; Ferguson & Lee, 2013 ; Nsamenang, 1992 ). The questionnaires developed for the MICS, an international household survey, may be a useful beginning. The involvement of key stakeholders living within each context studied will also be essential in this process. UNESCO’s Growing up in Cities project ( Chawla, 2002 ) provides a nice illustration of a participatory process in which research questions and assessment tools were developed jointly by researchers and community stakeholders.

Phase 2: Identifying leverage points and mechanisms

Bronfenbrenner noted that proximal processes, the exchanges of energy between the developing child and the persons and objects in her immediate settings, are the “engines of development” ( Bronfenbrenner & Morris, 1998 ; Bronfenbrenner & Evans, 2000 ). In order for these processes to be effective, they need to occur on a regular, sustained basis and become increasingly complex as the child matures. Given this, a starting point for identifying key leverage points is the identification of environmental factors that clearly interrupt proximal processes for children. Factors that contribute towards chaos, including noise, crowding, and residential mobility (partially instantiated by informal housing facilities), are likely candidates here, as they are likely to interfere with effective proximal processes ( Bronfenbrenner & Evans, 2000 ; Evans & Wachs, 2010 ). As we have discussed above, housing and school design may also contribute towards chaos, particularly when a large number of people live or study in a small number of open plan rooms. In addition, schools and neighborhoods characterized by high residential instability may contribute towards chaos at the macro level.

We have noted above that one of the unintended consequences of various coping strategies for dealing with crowding, noise, and chaos may be deteriorations in socially supportive relationships and less responsive parenting. The design of spaces, not simply the presence of stressors like chaos, can also influence interpersonal relationships, thus affording or inhibiting ease of interpersonal interactions. For example, are typical travel routes likely to lead to unplanned, impromptu interactions? Are there spaces that people feel comfortable spending time in such as cafes and common facilities (e.g., a communal laundry area, community play spaces)?

In addition to proximal processes such as parent-child interactions (e.g., responsiveness, monitoring), several other candidate mechanisms are worthy of further examination both in the global North and South. One of the common qualities of many of the suboptimal physical settings children encounter is their uncontrollability. We need more examination of mastery, self-efficacy and other control-related processes in relation to the environment and children’s development. Some of the ways in which physical settings can influence mastery include: uncontrollable stressors such as noise and crowding; highly unpredictable and variable conditions such as chaos; the degree of inflexibility and regimentation of settings such as school; the scale and manipulability of settings for children; and design and planning features that afford crime, such as undifferentiated spaces lacking in ownership and defensibility.

Considerable work shows that time spent in nature and other restorative spaces can help counteract cognitive fatigue and stress engendered by the fast-paced, multitasking demands of modern life, increasingly common throughout the world, regardless of economic development ( Kaplan & Kaplan, 1989 ). Fascination or the experience of involuntary attention (e.g., curiosity) is not the sole purview of natural elements but can include human-made objects and spaces that attract and hold attention effortlessly (e.g., people-watching in a plaza, gazing at a fountain, meandering through a museum or good bookstore, or enjoying street entertainment).

Stressors such as crowding, noise, traffic, and chaos can directly strain physical and psychological systems, but they also have the ability to alter regulatory processes such as coping and executive functioning ( Evans & Kim, 2013 ). Thus another area worthy of further scrutiny in considering children’s environments is the role of coping and self-regulatory processes. When children and their parents encounter various suboptimal environments, they often adapt strategies, be they behavioral, cognitive, or both, to right the balance between environmental demands and human comfort and well being. These adjustments and adaptations to the environment, in and of themselves, can lead to developmental changes. For example, parents who cope with too much unwanted social interaction by withdrawing from their children are likely to be less responsive.

The impact of the environment on adult caregivers is a particularly important underlying process to consider. Parents in crowded homes are typically less responsive and less patient ( Evans et al., 2001 ). Teachers in noisy schools report more fatigue and frustration, and observations of noisy schools show substantial reductions in teaching time ( Evans & Hygge, 2007 ). The stress and anxiety engendered by knowledge of toxic exposures or parental struggles with substandard housing are bound to translate into less than ideal parent-child interactions. Interestingly, such parent-child interactions may in fact modify children’s gene expression without altering the nucleotide sequence, as recent work in epigenetics has demonstrated ( Meaney, 2010 ).

Phase 3: Identifying and addressing key inequalities and opportunities

As we have discussed above, the final step involves interdisciplinary, international research teams both filling in the gaps in the research literature in understudied contexts and implementing interventions to improve children’s developmental functioning. One key leverage point for both cognitive and socioemotional development that might be further studied and then addressed is chaos. Implementing interventions within children’s home, school and neighborhood environments that reduce chaos and/or moderate its impacts on children may be a particularly effective way to improve children’s developmental outcomes. Interventions could include building sound barriers to block out aircraft and traffic noise, relocating homes and schools further from busy highways and airports, and redesigning open plan homes and classrooms to include quiet, secluded spaces for children.

One of the ways in which chaos has a particularly insidious effect is in its interruption of play, a key proximal process for young children’s cognitive and socioemotional development ( Bartlett, 1999 ; Milteer et al., 2012 ). Unsafe housing, school and neighborhood settings also disrupt play, as was coherently argued by the children living in as diverse contexts as Argentina, India, South Africa, Australia, the United Kingdom, the United States, Norway and Poland involved in UNESCO’s Growing Up in Cities project ( Chawla, 2002 ). Low-income urban children may be at particular risk for interruption of play processes ( Chawla, 2002 ; Milteer et al., 2012 ), and these same children frequently encounter multiple environmental risk factors in their home, school and neighborhood environments ( Bartlett et al., 1999 ; Evans, 2004 ). Thus building safe, green play spaces for low-income and other children across the global North and South will likely have a particularly positive impact on their cognitive and socioemotional development. In addition, the implementation of Community Adventure Play Experiences ( CDI, 2012 ), that is, temporary play spaces within children’s own communities that engage them in interactive play with recycled materials, may be a particularly low-cost and sustainable approach to increasing opportunities for child play in low-resource settings. In low-income and highly mobile communities, these may provide a good alternative to constructing new playgrounds, and have the added advantage that they can take place both indoors and outdoors. Such spaces may also provide common ground for community members to have greater social interaction, forming networks of relationships.

The physical environments experienced by children have important impacts on their cognitive and socioemotional development. Yet the work to date documenting these impacts in the global South is limited. We thus call for the development of a holistic, multidisciplinary and multilevel approach, based on Bronfenbrenner’s bioecological model, to the investigation of the impacts of the physical environment on child and adolescent development. Such work should be led by an interdisciplinary, international team of researchers in collaboration with local and national government agencies and community members, including the children themselves. This approach will allow us to more effectively intervene in the actual lived environments of both high- and low-income children across the globe.

Acknowledgments

This research was supported in part by grants from the W.T. Grant Foundation and the John D. and Catherine T. MacArthur Foundation. Special thanks to Jane Gorski for her help in locating and organizing reference materials and to Sheridan Bartlett for advice on this paper.

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Why parents must protect young children from excessive screen time

SINGAPORE - At KK Women’s and Children’s Hospital (KKH), doctors have seen children as young as 18 months who were exposed to screens for between two and six hours a day – and some of them had been exposed to screens since they were six to 10 months old.

Spending multiple hours on screens may be the norm for many adults but this should not be the case for children, particularly infants, as it can affect their overall development. 

Excessive screen time for children can lead to a myriad of issues, including myopia, speech delay, autism-like symptoms and obesity.

Dr Yvonne Ling, an eye surgeon specialising in adult squints and paediatric ophthalmology at the Singapore National Eye Centre, said that young children who spend hours indoors every day focusing on near work, such as viewing mobile devices and reading, may get myopia as early as in their pre-school years.

It may progress to very high myopia, which is a complex condition that can lead to blindness in adulthood, she added.

“The younger you get it, the higher it will be when you become an adult because the eye is still growing,” said Dr Ling, who also sees young patients at KKH.

Children should be spending some time outdoors, preferably around two hours every day, she said. “I used to agree with parents who are very busy and say, okay, you just take them out on weekends. But I realised that, actually, every day of the week, the eye is still growing.”

Near work induces the growing eyeball to elongate, resulting in a myopic or nearsighted eye. Activities outdoors under the sky and during daylight can stop, although not reverse, the elongation, Dr Ling said.

Looking into the far distance – at the clouds and sky, for instance – would also help, she added.

Dr Christelle Tan, a consultant in paediatric medicine at KKH’s Department of Child Development, said children who spend too much time on screens may experience other possible negative effects, including speech delay in toddlers, poor social interaction and reduced attention span.

She said KKH gets continual referrals of infants aged 18 months and older who experience speech delay.

“Their parents tell us that these children have been exposed to screens for two to three, sometimes, even five to six hours a day since they were six to 10 months old,” said Dr Tan.

While screen time can be beneficial for children, it is not the case for infants below 18 months of age. Local guidelines, as with those overseas, state that they should not be exposed to screens, unless it is for video chatting with a family member.

Measures to deal with device usage in Singapore will be released in the coming months, Health Minister Ong Ye Kung and Minister for Social and Family Development Masagos Zulkifli said in June.

Research has shown that exposing children between one and 1½ years old to screens led to poorer cognitive development including language delay, social interaction difficulties and reduced attention span, Dr Tan said, adding that “content in screens is of no help to those below 18 months”.

“Some studies have seen effects lasting beyond eight years of age for these infants exposed when they were one year old,” she said.

At KKH, there are children, aged from three to 10, who have symptoms that look like attention deficit hyperactivity disorder (ADHD) such as reduced attention capacity.

“Excessive screen time doesn’t cause ADHD, but it can lead to symptoms that are quite similar in that it can shorten the child’s ability to focus on a task and affect his or her executive functioning, which is how he or she plans, organises and completes multi-step tasks,” said Dr Tan.

Unlike the symptoms of ADHD, which is a brain disorder that results from an imbalance of neurotransmitters (brain chemicals that help nerve cells communicate with each other), ADHD-like symptoms gradually go away with controlled screen time, she said. 

Dr Ling and Dr Tan are guests in an upcoming ST Health Check podcast on the impacts of excessive screen time on children and why parents must do something about this.

The episode will be released on July 3 on the ST Health Check channel, the Straits Times website, Apple podcasts and Spotify.

What is screen time?

It is the time spent on devices with screens such as TV, smartphones, tablets, computers and learning devices. It includes having the TV play in the background, which can lead to a decrease in high-quality interpersonal communication and overstimulation. Screen time can support learning, development and play, but it must be balanced with other activities.

How much is enough?

Under 18 months: No screen time except for video calling From 18 months to six years old: Less than an hour a day, with co-viewing preferred From seven to 12 years old: Make a collaborative screen use plan to ensure a healthy balance between screen exposure and other age-appropriate activities.

What are the negative impacts?

Data shows that children in Singapore are becoming short-sighted from a younger age, even as young as in pre-school, putting them at risk of high myopia in adulthood. Those with very high myopia are more vulnerable to serious complications that can lead to severe vision loss or blindness in adulthood.

Speech delay

This can happen to infants who are exposed to screen use from birth and do not have enough one-on-one communication with another child or an adult. As infants learn the most from human interaction, they should have opportunities to engage and play with people.

Autism-like symptoms

Some signs of too much screen time in children may be poor social interaction, decreased eye contact and a lack of response to their names. These symptoms, similar to those of autism spectrum disorder, can resolve with less screen time.  

Screen time can displace outdoor play and physical movement that strengthens muscles and bones. The lack of physical activity may lead to obesity, putting children at increased risk of getting chronic health issues such as high blood pressure earlier in adulthood.

Dry and tired eyes

​Prolonged screen time can leave eyes feeling dry and tired. Blurry vision and headaches can also occur. These are short-term problems that do not cause eye damage. However, a more serious condition of double vision due to misalignment of the eyes, or squint, is surfacing even among young children. This type of double vision occurs when both eyes are being used and disappears when either eye is closed.

Reduced attention span

Children given too much screen time are easily distracted and have difficulty staying on a task. Parents may start to see this in their pre-school or lower primary children. 

The blue light from screens may affect the initiation of sleep, so screen use should be stopped at least one hour before bedtime. Excessive screen use has been associated with poorer sleep quality and sleep disturbances. 

Aggressive behaviour

After viewing violent content or playing certain games, some children may exhibit violent behaviour, such as punching or making stabbing motions. This stems from a lack of maturity to filter out inappropriate content or understand context. 

Neck and back strain

This is not a common complaint among children, but it can happen from too much time spent in a slouched position, with the head held forward. When this same position is held for very long periods of time, it can strain the muscles and joints in the back of the neck and in the back.

Mental health concerns

Depressive symptoms, such as low mood, may be seen in older children in upper primary or secondary school. Other issues such as anxiety can also develop.

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Measuring well-being and progress

GDP is a well-established tool for measuring economic output, but it does not tell us whether life as a whole is getting better, and for whom. The OECD Well-being Framework helps to monitor societal progress “beyond GDP” and is informing people-centric and integrated policy making across the many dimensions that matter for people, the planet and future generations. The Framework provides a compass to understand how human well-being is evolving in the context of the ecological and digital transitions, and what key actions are needed to support it.

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Key messages, improving people’s well-being requires balancing economic, social and environmental objectives and focusing on relationships and social connectedness.

Assessing the well-being of individuals, communities and societies requires looking at multiple aspects that matter for people’s lives. This holistic perspective is even more relevant in a context of fast societal changes. The OECD Well-being Framework considers current well-being outcomes – and inequalities in these – to capture the material conditions that shape people’s economic options, their quality of life, and their relationships. The Framework also includes the systemic resources that are needed to sustain well-being in the future and within planetary and social boundaries. The Framework can support governments and other actors to design policies that promote synergies between economic, social and environmental goals and that put a primary focus on promoting mental health at individual and community level.

The OECD collects statistics on the key components of well-being to monitor people’s quality of life and relational well-being in the context of the digital, demographic and green transitions and to help countries understand whether life is getting better and whether the benefits of progress are being shared equally.

Countries have started to collect internationally harmonised statistics on well-being and should expand this practice further

Frequent, timely and high-quality data on well-being is essential to inform policy decisions. The OECD is advancing the statistical agenda by supporting data producers with methodological guidance on new frontiers of well-being measurement, including subjective well-being, trust, mental health and social connectedness. Although more work remains to be done, this has helped to close data gaps, especially in dimensions of life where internationally harmonised well-being data is most scarce.

The OECD is continuing to update its advice to reflect the latest evidence. For instance, the 2013 Guidelines on Measuring Subjective Well-being are currently being expanded to include guidance on child subjective well-being as well as more globally inclusive measures. 

A well-being lens can bring a more integrated perspective to policy challenges, such as mental health

Mental health affects every aspect of life and is influenced by people’s economic, social and environmental living conditions. However, despite mental health’s strong interactions with factors such as income, education, employment and the environment, integrated approaches across government departments remain limited or small-scale. Reasons include inter-departmental task forces often being time-limited and without decision-making power and resource constraints remain a challenge.

The OECD uses a well-being lens to underscore the reciprocal relationships between mental health and socio-economic outcomes and shows how policies to promote mental health can contribute to achieving other social, economic and environmental policy goals.

OECD governments are increasingly using well-being evidence to inform their policy practices

The real pressure test for well-being initiatives is whether they will be able to graduate from “yet another report” to tangibly influencing government decision-making, and ultimately, people’s quality of life. Multidimensional well-being frameworks and concepts are increasingly being employed by OECD countries in budgeting, policy appraisal and evaluation, strategic coordination, and performance management. Mainstreaming well-being in policy is not a simple add-on to existing practice: it requires and supports new ways of thinking and acting that are more people-focused, more long-term and more joined-up across economic, social, and environmental policy objectives.

In 2023, the OECD launched the Knowledge Exchange Platform on Well-being Metrics and Policy Practice to provide a space for sharing experiences and solutions and to support governments interested in developing policy-focused well-being initiatives. 

How’s life in your country?

The OECD has developed innovative data visualisations that help to illustrate each country’s relative well-being strengths and weaknesses across themes like income, work and job quality, housing conditions, health, safety, social connectedness and civic engagement. The longer a bar in the “well-being wheel”, the better the outcomes in a specific area of well-being, compared to other OECD countries.

Mental health shapes many aspects of life

The OECD used the dimensions of its Well-being Framework to illustrate how economic, social and environmental outcomes intersect with mental health. People experiencing worse mental health tend to fare far worse in most other aspects of their well-being. For example, compared to the general population, those at risk of mental distress are nearly twice as likely to be at the bottom of the income distribution, to be unemployed, or to be unhappy with how they spend their time; their risk for feeling lonely is more than four times greater.

Related content

• Tool OECD Better Life Index There is more to life than the cold numbers of GDP and economic statistics – This Index allows you to compare well-being across countries, based on 11 topics the OECD has identified as essential, in the areas of material living conditions and quality of life. oecdbetterlifeindex.org
• OECD Data Explorer data-explorer.oecd.org

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• Well-being and beyond GDP To understand whether policies are improving lives we need to look "beyond GDP" and consider a broader range of economic, social and environmental outcomes for people. This also allows to understand what matters to people and what drives their behaviours, providing another channel of action to policies. The OECD is leading efforts to develop indicators that measure the well-being of individuals, families, society, future generations and the planet at a time of deep changes and transformations. Learn more